Research ArticleNEUROSCIENCE

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

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Science Advances  16 Nov 2016:
Vol. 2, no. 11, e1601335
DOI: 10.1126/sciadv.1601335
  • Fig. 1 Scheme of the large-scale model.

    The scheme shows the four levels considered: a within-layer local microcircuit consisting of an excitatory (Excit.; in red) and an inhibitory (Inhib.; in blue) population (upper left), a laminar circuit with two laminar modules (corresponding to supra- and infragranular layers, lower left), an interareal circuit with laminar-specific projections (lower right), and a large-scale network of 30 cortical areas based on macaque anatomical connectivity (upper right). Each level is anatomically constrained, and its dynamics provide insight into different electrophysiological observations in macaques. Only the connections at each level not shown at a lower level are plotted, for clarity.

  • Fig. 2 Local circuit model at the intralaminar level.

    (A) Scheme of the local circuit (top), with the excitatory and inhibitory population in red and blue, respectively, and examples of the oscillatory activity for an excitatory-inhibitory circuit in layer 2/3 (middle, in green) and layer 5/6 (bottom, in orange). (B) Power spectrum of the firing rate of an isolated layer 2/3 as a function of input strength to the excitatory population. The spectrum of the spontaneous state (with zero input) has been subtracted in each case to highlight changes induced by the input (see main text). As the input increases (which resembles the effect of increasing the contrast of a visual stimulus), the power of gamma rhythms becomes stronger, as in observations by Henrie and Shapley (13). (C) Effect of the input to the excitatory population on the power spectrum peak (left) and frequency (right) of the oscillations, for an isolated layer 2/3 (top) and an isolated layer 5/6 (bottom).

  • Fig. 3 Cortical area model at the interlaminar level.

    (A) Scheme of the interlaminar circuit (left panel); self-connections within a given population are omitted in the figure for clarity. Interlaminar connections considered in the model correspond to the strongest projections between layer 2/3 and layer 5/6 as found in experimental studies. Right: Power spectrum of layer 2/3 (top) and layer 5/6 (bottom) in the case of uncoupled, isolated layers (in black, for comparison) and interconnected network (green and orange, respectively). A background input of I = 8 was fed into the excitatory population of both layers. (B) Bottom: A set of 30 traces of activity in layer 5/6 (in gray) and their average (in blue). The central peak of each trace was aligned at zero before averaging. Top: A periodogram of layer 2/3 showing the averaged power for a range of frequencies for the same temporal periods as the layer 5/6 traces. We can see the existence of a strong entrainment of gamma power to alpha phase, as in the experimental findings by Spaak et al. (16). Input was I = 6 for supragranular and I = 8 for infragranular excitatory populations. (C) Effect of injecting external current to the excitatory population of layer 5/6 on the layer 2/3 gamma power and (dimensionless) firing rate (top left and right, respectively) and on layer 5/6 alpha power (bottom left). An inverse relationship between supragranular firing rate and alpha power is observed (bottom right), which highlights a possible link of enhanced alpha rhythms with activity suppression.

  • Fig. 4 Microstimulation at the interareal level.

    (A) Scheme of the interareal projections between two areas (V1 and V4); the anatomical hierarchy ascends from left to right. We inject a current of I = 15 at both supra- and infragranular excitatory populations of V1 and measure at V4. In addition to this input, a background current to excitatory populations in supragranular (I = 2) and infragranular (I = 4) layers in both V1 and V4 is injected to guarantee a minimum level of activity. (B and C) Power spectrum at V4 measured at layer 2/3 (B) and layer 5/6 (C), for resting and stimulation conditions. Insets show the peak value of the power spectrum at supragranular (B) and infragranular (C) layers for the same resting and stimulation conditions. A large increase in gamma power is found, as in the microstimulation experiments by van Kerkoerle et al. (3). (D) Same as (A), but injecting a current of I = 15 at all excitatory populations of V4 and recording in V1. In addition, a background current of I = 1 to all excitatory areas in V1 and V4 is injected to guarantee a minimum of activity. (E and F) Power spectrum at V1 layer 2/3 (E) and layer 5/6 (F) for resting and stimulation conditions. Inset shows the peak value of the power spectrum for these conditions. A large increase in alpha power and a decrease in gamma power were found, in agreement with experimental observations. For (B) and (F), the blue curve corresponds to an isolated area receiving the same input as in the stimulation case, but without its rhythmic component (see main text for details). n.s., not significant. ***P < 0.001.

  • Fig. 5 Frequency-specific FF and FB interactions.

    (A) We now inject current and record the activity of both areas (V1 and V4). An input current of I = 8 was injected in all excitatory populations of the circuit. (B) Spectral coherence between V1 and V4 activity highlights the existence of two peaks at the alpha and gamma range, respectively. (C) Spectral pairwise GC in the V1-to-V4 (green) and V4-to-V1 (orange) directions, showing that each of the peaks found in (B) corresponds to a particular direction of influence, suggesting that frequency-dependent GC analysis could be used to deduce FF versus FB signaling directionality between areas for which the hierarchical positions are not known anatomically. (D) The DAI profile of the functional connection, which is obtained by normalizing the difference between the two GC profiles in (C), can be used to characterize a directed functional connection between two cortical areas.

  • Fig. 6 Large-scale cortical network and functional hierarchy.

    (A) Illustration of the anatomical tract-tracing technique used to obtain the anatomical large-scale network and, in particular, the fraction of supragranular labeled neurons (or SLNs, see main text for details). A high (low) value of SLN for a given projection indicates that the source area is lower (higher) than the target area (the injected area) in the anatomical hierarchy. (B) 3D plot of the macaque anatomical network obtained (only projections with FLN of >0.005 are plotted, for clarity), with all 30 areas in their spatial positions. Connection strength is indicated by line width. (C and D) alpha (C) and gamma (D) power for eight selected cortical areas of interest (V1, V2, V4, DP, 8m, 8l, TEO, and 7A). (E) Correlation between SLN and DAI, as a function of frequency. The correlation is positive in the gamma range and negative in the alpha range, indicating a prevalent involvement of these rhythms in FF and FB interactions, respectively. (F) Correlation between SLN and the combined DAI across gamma (30 to 70 Hz) and alpha/low-beta (6 to 18 Hz) frequency ranges (named mDAI, see text for details). (G) Functional hierarchy emerging from the frequency-specific interactions in the network and computed using the mDAI values as in Bastos et al. (5). (H) Areas belonging to the same type (early visual, ventral, dorsal, or frontal; indicated by color box) tend to be clustered in the same way as in the experimental observations. For all panels, visual input was simulated with an input current I = 8 to the supragranular excitatory population of V1, and in addition to this input, a background current of I = 6 to all excitatory populations in the network was also considered.

  • Fig. 7 Mechanistic explanation for the experimentally observed hierarchical jumps.

    (A) Scheme of the simple two-area circuit considered (we use the same parameters as for the two-area microstimulation protocol); the area on the left, which is lower in the anatomical hierarchy, receives laminar-specific input. (B) The gamma component of the DAI increases as the input to layer 2/3 exceeds input to layer 5/6 for the lower area (ascending curves correspond to input to L2/3E increasing from I = 4 to 6 to 8, and input to L5/6E decreasing from I = 8 to 6 to 4). In the higher area, excitatory populations receive a fixed I = 6 background current. (C) Increases in the hierarchy rank of the higher area as a consequence of the laminar-specific input in (B). The laminar specificity S, defined as the difference between the input to L2/3E and to L5/6E, goes from −4 to 0 to 4 in this example. (D) We follow the same procedure but now injecting laminar-specific current into area 8l within the full 30-area network. (E and F) Changes in alpha (E) and gamma (F) band power as a consequence of the injection of laminar-specific input. Lines in gray correspond to the same input injected at both layers (that is, no laminar-specific input), whereas colored lines correspond to a laminar specificity of S = 8. (G) The spectral DAI profile from 8l to 8m increases in the gamma range as a consequence of the laminar-specific input (gray curve, S = 0; blue curve, S = 8). (H) A hierarchical jump of area 8m is observed, as in the two-area case (gray points, S = 0; blue points, S = 8). (I) We find a robust increase of the hierarchical jump distance with the strength of the laminar specificity of the input. Other parameters and background currents are as in Fig. 6.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/11/e1601335/DC1

    Supplementary Methods

    fig. S1. Microstimulation experiments at the interareal level, for an FB projection that is strong to L2/3E and weak to L5E (more precisely, with a supra/infra ratio of 0.8).

    fig. S2. FLN connectivity matrix, after a logarithmic transformation for visualization purposes, for the 30 areas of the large-scale model.

    fig. S3. SLN connectivity matrix for the 30 areas of the large-scale model.

    fig. S4. Wiring distances, in millimeters, for the 30 areas of the large-scale model.

    fig. S5. Anatomical hierarchy obtained from the data shown in fig. S3.

    fig. S6. Different matrices for the subset of eight areas of interest (V1, V2, V4, DP, 8m, 8l, TEO, and 7A) used in the functional hierarchy study.

    fig. S7. Spectral pairwise-conditioned GC profiles for all the possible pairs of interactions between the eight cortical areas of interest: V1, V2, V4, DP, 8m, 8l, TEO, and 7A, with a background input of I = 6 to all areas, plus a strong extra input of I = 6 to V1.

    fig. S8. Effect of introducing a distance-dependent relationship on the target of FB projections.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Methods
    • fig. S1. Microstimulation experiments at the interareal level, for an FB projection that is strong to L2/3E and weak to L5E (more precisely, with a supra/infra ratio of 0.8).
    • fig. S2. FLN connectivity matrix, after a logarithmic transformation for visualization purposes, for the 30 areas of the large-scale model.
    • fig. S3. SLN connectivity matrix for the 30 areas of the large-scale model.
    • fig. S4. Wiring distances, in millimeters, for the 30 areas of the large-scale model.
    • fig. S5. Anatomical hierarchy obtained from the data shown in fig. S3.
    • fig. S6. Different matrices for the subset of eight areas of interest (V1, V2, V4, DP, 8m, 8l, TEO, and 7A) used in the functional hierarchy study.
    • fig. S7. Spectral pairwise-conditioned GC profiles for all the possible pairs of interactions between the eight cortical areas of interest: V1, V2, V4, DP, 8m, 8l,
      TEO, and 7A, with a background input of I = 6 to all areas, plus a strong extra input of I = 6 to V1.
    • fig. S8. Effect of introducing a distance-dependent relationship on the target of FB projections.

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