Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Abstract

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding predictive coding, executive control and a gamut of other brain functions. The underlying circuit mechanism, however, remains poorly understood and represents a major challenge in neuroscience. In the present work we tackled this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intra-laminar, inter-laminar, inter-areal and whole cortex) scales. The model was tested by reproducing, and shedding insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas: feedforward pathways are associated with enhanced gamma (30-70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low beta (8-15 Hz) oscillations. We found that in order for the model to account for the experimental observations, the feedback projection needs to predominantly target infragranular layers in a target area, which leads to a proposed circuit substrate for predictive coding. The model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of inter-areal signaling, as reported in recent monkey and human experiments. Taken together, this work highlights the importance of multi-scale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

“…For instance, deficient local PAC could be related to long-range connectivity in form of insufficient low frequency coupling across brain areas that in turn may not succeed in entraining high frequencies locally (e.g. Arnal & Giraud, 2012;Mejias et al, 2016;Onslow et al, 2014;Peterson & Voytek, 2015), which might also be reflected in deficient inter-trial phase coherence in low and high frequencies. Finally, even if such a mechanistic link was identified reliably, the necessity and the role of PAC and long-range coupling for cognitive function will have to be addressed within a consistent theoretical framework of ASD.…”

Mechanisms of distributed working memory in a large-scale network of macaque neocortex

“…1b, d. Areas in these matrices are arranged following the anatomical hierarchy, which is computed using the SLN values and a generalized linear model (Chaudhuri et al, 2015;Mejias et al, 2016). Surgical and histology procedures were in accordance with European requirements 86/609/EEC and approved by the ethics committee of the region Rhone-Alpes.…”

“…1c). To allow for the propagation of activity from sensory to association areas, inter-areal long-distance connections target more strongly excitatory neurons than inhibitory neurons for more feedforward pathways, but biased in the opposite direction for more feedback pathways, in a graded fashion (Mejias et al, 2016; Fig. 1b).…”

Dysregulated Oscillatory Connectivity in the Visual System in Autism Spectrum Disorder

“…In particular, phase-amplitude coupling (PAC) has been proposed to act as a mechanism for the dynamic co-ordination of brain activity over multiple spatial scales, with the amplitude of high-frequency activity within local ensembles coupled to largescale patterns of low-frequency phase synchrony (Bonnefond et al, 2017). Alpha-gamma PAC is also closely tied to the balance between excitatory and inhibitory (E-I) populations of neurons (Mejias et al, 2016), which is affected in autism (Rubenstein and Merzenich, 2003).…”