The role of anatomical connection strength for interareal communication in macaque cortex

Abstract

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

A mechanism for inter-areal coherence through communication based on connectivity and oscillatory power

“…Previous work has shown correlations between functional and anatomical connectivity. Consistent with SSM model predictions, anatomical connection strength is linearly correlated with LFP coherence/Granger causality across brain areas in beta and gamma bands (Vezoli et al, 2020). Feedforward and feedback Granger causality in gamma and beta frequency bands correlate with the anatomical SLN measure (% of supragranular-labeled neurons) (Bastos et al, 2015).…”

“…We first analyzed data from two macaque brain areas showing clear LFP beta-coherence during a visually cued delayed grasping task. During motor holding and working memory periods, parietal cortex contains a prominent source of beta oscillations ( Scherberger et al., 2005 ; Hagan et al., 2012 ; Murthy and Fetz, 1996 ; Donoghue et al., 1998 ), with strong Granger-causality influences toward visual and frontal areas ( Brovelli et al., 2004 ; Bastos et al., 2015 ; Vezoli et al., 2020 ). These long-range interactions in the beta band are thought to play a role in working memory, attention, and predictive processing ( Salazar et al., 2012 ; Buschman and Miller, 2007 ; Bastos et al., 2015 ; Roelfsema et al., 1997 ; Brovelli et al., 2004 ).…”

Local and long-distance organization of prefrontal cortex circuits in the marmoset brain

“…The laminar profile of axon terminals has been used to infer hierarchical relationships between connected areas [31][32][33][34] . To investigate this issue in our data, we classified the laminar patterns of columnar patches by hierarchical clustering (Fig.…”

Directed Functional and Structural Connectivity in a Large-Scale Model for the Mouse Cortex

“…First, modeled neuronal population parameters are drawn from the same distributions with activity in the gamma band range (Figure 1). The activity of cortical areas in mice occurs in multiple frequency ranges [63,64] and the relationship between structural and functional connectivity depends on the frequency [27]. A second limitation is that we do not model changes in network states, which are known to influence functional connectivity [65].…”

“…was measured as the ratio of the number of labeled neurons in a source area to the total quantity of labeled neurons in all source areas, where labeled neurons considered are extrinsic to the injected area [25,26,27]. We defined the edge measure FLN ij as the number of neurons projecting from area j to area i, divided by the number of neurons projecting to area i from all the areas except i [28,29].…”