A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex

Abstract

We developed a large-scale dynamical model of the macaque neocortex, which is based on recently acquired directed- and weighted-connectivity data from tract-tracing experiments, and which incorporates heterogeneity across areas. A hierarchy of timescales naturally emerges from this system: sensory areas show brief, transient responses to input (appropriate for sensory processing), whereas association areas integrate inputs over time and exhibit persistent activity (suitable for decision-making and working memory). The model displays multiple temporal hierarchies, as evidenced by contrasting responses to visual versus somatosensory stimulation. Moreover, slower prefrontal and temporal areas have a disproportionate impact on global brain dynamics. These findings establish a circuit mechanism for "temporal receptive windows" that are progressively enlarged along the cortical hierarchy, suggest an extension of time integration in decision making from local to large circuits, and should prompt a re-evaluation of the analysis of functional connectivity (measured by fMRI or electroencephalography/magnetoencephalography) by taking into account inter-areal heterogeneity.

Figure 1

The network consists of 29 widely-distributed cortical areas. (A) Lateral (left) and medial (right) plots of the macaque cortical surface with areas in color. Plots generated with Caret (Van Essen et al., 2001). (B) Connection strengths between all 29 areas. The strength of the projection from area A to area B is measured by the Fraction of Labeled Neurons or FLN (see Experimental Procedures and Table S1). (C) Three dimensional positions of areas along with strongest connections between them (FLN > 0.005). Connection strength is indicated by line width.

Figure 2

Hierarchical organization of the cortex. (A) Fraction of neurons in a projection originating from the supragranular layers of the source area (SLN). Areas are arranged by hierarchical position. Thus most feedforward projections (SLN>0.5) lie below the diagonal and most feedback projections (SLN < 0.5) lie above the diagonal. Absent projections shown in grey. (B) Hierarchical position of an area is well-correlated with the number of spines on pyramidal neurons in that area (Elston, 2007). For details on area labels in this panel see Supplemental Experimental Procedures. (C) Two-dimensional plot of areas determined by long-range connectivity and hierarchy. The distance of an area from the edge corresponds to its hierarchical position, while the angular distance between two areas is inversely related to their connection strength. Areas are colored by cortical lobe. See also Figure S1, and Table S1 for the data.

Figure 3

The network shows a hierarchy of timescales in response to visual input. (A) A pulse of input to area V1 is propagated along the hierarchy, displaying increasing decay times as it proceeds. In all panels, areas are arranged (and colored) by position in the anatomical hierarchy. (B) Traces contrasting the activity of area V1 and dorsolateral prefrontal cortex in response to white-noise input to area V1. (C) Autocorrelation of area activity in response to white-noise input to V1. The autocorrelation decays with different time constants in different areas, showing a functional hierarchy ranging from area V1 at the bottom to prefrontal areas at the top. (D) The dominant time constants in various areas of the network, extracted by fitting exponentials to the autocorrelation (colors as in C). Time constants tend to increase along the hierarchy but depend on the influence of long-range projections (for example, contrast area 8m with area TEpd). See also Figures S2 and S3.

Figure 4

The response to somatosensory input reveals a different functional hierarchy subserved by the same anatomical network. (A) Autocorrelation of activity for areas that show strong responses to input to area 2 (part of primary somatosensory cortex). Area labels are arranged according to position in the underlying anatomical hierarchy. Inset: time constants fitted to the autocorrelation function for each area. (B) Timescales in response to visual (left) and somatosensory input (right) shown with lateral (top) and medial (bottom) views of the cortex. See also Figure S4.

Figure 5

Role of local and long-range projections in determining timescales. (A) Time-constants fit to network activity after removing gradient of excitation or long-range projections. Far left: time constants for intact network. Center left: network with no gradient of excitatory synapses across areas. Center right: network with feedback projections lesioned. Far right: network with all long-range projections lesioned. (B) Effect of scrambling long-range connectivity on resting-state network dynamics, measured by the time taken for an area’s activity to return to 5% of baseline after a 250 ms pulse of input. Distribution of timescales when all connection strengths are randomly permuted. Dark blue, lighter blue and very light blue circles indicate median value, 10th to 90th percentiles and 5th to 95th percentiles respectively. Intact network shown in black, for comparison. Timescales for scrambled networks are much more similar to each other (compare black to blue), and fast visual areas show the greatest disruption. (C) Distributions when only non-zero connection strengths are permuted, thus preserving the connectivity pattern but not strengths.

Figure 6

Eigenvectors of the network coupling matrix are weakly localized, corresponding to segregated temporal modes. Each column shows the amplitude of an eigenvector at the 29 areas, with corresponding timescale labeled below. The 29 slowest eigenvectors of the system are shown.

Figure 7

Hierarchy of timescales in a nonlinear model. (A) Possible steady-states (bifurcation diagram) for an area as a function of recurrent strength (normalized by value at V1). Stable steady-states are shown with solid lines. Areas with comparatively low recurrent strength display only a single steady-state. Increasing the recurrent strengths leads to a regime with a high-activity steady-state. The dashed line is an unstable intermediate steady-state. The thick blue line shows the parameter range supporting bistability, while the light blue shaded region indicates the range used for areas in the model. Steady-states shown as fractional activation of NMDA conductance. (B) Response of disconnected areas to a strong pulse of input. As in (A), V1 only shows a single stable state, while area 24c shows sustained delay activity. (C) The timescales of responses to a small perturbation serve as a probe of the recurrent strength of a local area. These timescales are much smaller than those in response to a larger input but emerge from the same underlying gradient in recurrent strengths. (D) Response of connected network to a brief pulse of input to area V1. As in Figure 3, the input is propagated up the hierarchy, slowing down as it proceeds. Note that the input is not strong enough to switch any area into the high-activity stable state.

Figure 8

Functional connectivity depends on local microcircuitry. (A) Functional connectivity for two networks with identical long-range connectivity. The network on the left has the same properties at each area, while that on the right has a gradient of local recurrent strengths. Top panel: correlations in area activity for uncorrelated background input to each area. Bottom panel: functional connectivity (correlation) vs. structural connectivity (FLN) for non-zero projections. The network with a gradient of local recurrence has enhanced functional connectivity for slow areas, and a smaller overall correlation between functional and anatomical connectivity (showing that long-range connections alone cannot predict global brain activity patterns). (B) Effect of lesioning areas, one at a time, on functional connectivity. Left panel: Darker areas are those with a greater influence on resting-state functional connectivity. Right panel: The effect of lesioning an area on functional connectivity is well-correlated with the time constant of spontaneous fluctuations in that area. See also Figures S5 and S6.