Elsevier

NeuroImage

Volume 108, March 2015, Pages 460-475
NeuroImage

Technical Note
A DCM study of spectral asymmetries in feedforward and feedback connections between visual areas V1 and V4 in the monkey

https://doi.org/10.1016/j.neuroimage.2014.12.081Get rights and content
Under a Creative Commons license
open access

Highlights

  • We briefly review evidence for canonical microcircuits (CMC) and predictive coding.

  • This evidence is incorporated into a novel Dynamic Causal Model (DCM).

  • We model observed cross-spectral densities from monkey visual cortex (V1 and V4).

  • We establish the face and predictive validity of this new DCM.

  • Gamma rhythms subserve feedforward, and alpha/beta rhythms feedback influences.

Abstract

This paper reports a dynamic causal modeling study of electrocorticographic (ECoG) data that addresses functional asymmetries between forward and backward connections in the visual cortical hierarchy. Specifically, we ask whether forward connections employ gamma-band frequencies, while backward connections preferentially use lower (beta-band) frequencies. We addressed this question by modeling empirical cross spectra using a neural mass model equipped with superficial and deep pyramidal cell populations—that model the source of forward and backward connections, respectively. This enabled us to reconstruct the transfer functions and associated spectra of specific subpopulations within cortical sources. We first established that Bayesian model comparison was able to discriminate between forward and backward connections, defined in terms of their cells of origin. We then confirmed that model selection was able to identify extrastriate (V4) sources as being hierarchically higher than early visual (V1) sources. Finally, an examination of the auto spectra and transfer functions associated with superficial and deep pyramidal cells confirmed that forward connections employed predominantly higher (gamma) frequencies, while backward connections were mediated by lower (alpha/beta) frequencies. We discuss these findings in relation to current views about alpha, beta, and gamma oscillations and predictive coding in the brain.

Keywords

Neuronal
Connectivity
Computation
Dynamic causal modeling
Synchronization coherence
Transfer functions
Gamma oscillations
Beta oscillations

Cited by (0)