Hippocampal and prefrontal processing of network topology to simulate the future

Amir-Homayoun Javadi, Beatrix Emo, Lorelei R Howard, Fiona E Zisch, Yichao Yu, Rebecca Knight, Joao Pinelo Silva, Hugo J Spiers, Amir-Homayoun Javadi, Beatrix Emo, Lorelei R Howard, Fiona E Zisch, Yichao Yu, Rebecca Knight, Joao Pinelo Silva, Hugo J Spiers

Abstract

Topological networks lie at the heart of our cities and social milieu. However, it remains unclear how and when the brain processes topological structures to guide future behaviour during everyday life. Using fMRI in humans and a simulation of London (UK), here we show that, specifically when new streets are entered during navigation of the city, right posterior hippocampal activity indexes the change in the number of local topological connections available for future travel and right anterior hippocampal activity reflects global properties of the street entered. When forced detours require re-planning of the route to the goal, bilateral inferior lateral prefrontal activity scales with the planning demands of a breadth-first search of future paths. These results help shape models of how hippocampal and prefrontal regions support navigation, planning and future simulation.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1. Illustration of the three centrality…
Figure 1. Illustration of the three centrality measures in a sample network.
The network was chosen to illustrate how the three measures of centrality record different properties of the network. Note each measure identifies different streets as having the highest value. (a) The highest degree centrality street reflects the fact that this street has six streets connected to it. (b) The highest closeness centrality streets reflect the fact that these streets are topologically closest to all other streets in the network. (c) The highest betweenness centrality street indicates that this street would be travelled most frequently when travelling from any one street to another.
Figure 2. Graph-theoretic analysis of London (UK)…
Figure 2. Graph-theoretic analysis of London (UK) street network centrality and the fMRI navigation task.
(a) Plots of central London (UK) street segment centrality measures (degree, closeness and betweenness). We used a segment-based approach known as space syntax. Here degree centrality measures the number of connecting segments to any segment, closeness measures how far any two segments are and betweenness measures the number of shortest paths from all segments to all other segments that pass through that segment. See Supplementary Table 1 for the relationship between measures in Soho. White bounded region in each plot indicates the region of Soho learned and navigated during fMRI scanning. See Supplementary Fig. 1 for the frequency of each value of centrality for Central London and this region of Soho. (b) Plots of segment centrality measures for the streets navigated in Soho. Thicker lines display an example of one of the 10 routes navigated during fMRI. (c) Top: degree centrality of the street segments in the example route plotted with each of the six Street Entry Events marked. Bottom: movie frames from our fMRI navigation task at the six Street Entry Events in the example route above.
Figure 3. Posterior hippocampal activity is correlated…
Figure 3. Posterior hippocampal activity is correlated with the change in degree centrality during navigation.
(a) Top left: degree centrality plotted for each street segment for an example route (see Fig. 2c). Right: axonometric projection of the buildings in Soho plotted on a map of Soho. Degree centrality of the route is plotted on the map and projected above. Above the route the graph plots the change in degree centrality for each boundary transition and the top graph plots the evoked response in the right posterior hippocampus at each of the individual boundary transitions (1–6). Analysis of this plot was not used for statistical inference (which was carried out within the statistical parametric mapping framework), but is shown to illustrate the analytic approach. (b,c) Right posterior hippocampal activity correlated significantly with the change in degree centrality for Navigation and Navigation>Control during Street Entry Events. Statistical parametric maps are displayed with threshold P<0.005 uncorrected on the mean structural image. (d) Parameter estimates for the mean activity in the right posterior hippocampus ROI for Navigation (t23=4.24, P=0.0003), Control (t23=1.17, P=0.25) and Navigation>Control (t23=4.64, P=0.0001) comparisons for a model containing categorical change in degree centrality (see Supplementary Table 2). (e) Parameter estimates for the mean activity in the right posterior hippocampus ROI for Navigation>Control condition for a model containing degree centrality (t23=2.28, P=0.03), betweenness centrality (t23=0.53, P=0.59) and closeness centrality (t23=0.14, P=0.88) measures (Supplementary Table 3 and Supplementary Fig. 3). Error bars denote the s.e.m. See Supplementary Fig. 4C for anterior hippocampal ROI mean responses.
Figure 4. Posterior hippocampal activity correlated with…
Figure 4. Posterior hippocampal activity correlated with the change in degree centrality specifically at Street Entry Events.
Top: perspective view of Soho showing part of the example route (Fig. 2a) shown to illustrate the three examples of the different time points examined. During navigation routes, right posterior hippocampal activity was significantly more correlated with the change in degree centrality at Street Entry Events than at Decision Points (t23=2.34, P=0.02) or at Travel Period Events (t23=4.01, P=0.001), *Significance at a threshold of P<0.05 corrected for ROI. Error bars denote the s.e.m.
Figure 5. Inferior lateral prefrontal activity correlates…
Figure 5. Inferior lateral prefrontal activity correlates with the demands of a breadth-first search at Detours.
(a) Diagrams of an example street network contrasting scenarios of lower and higher demand breadth-first search. Breadth-first search assumes the search space (street segments) as a tree and considers all possible solutions within one level before proceeding to the subsequent level. In these diagrams, covering the first layer of the search, the lower demand scenario shows less possible paths, while the higher demand scenario shows a greater number of possible paths. For details see Methods. (b) The statistical parametric map showing correlation (P<0.05 FWE-corrected) of the left and right lateral PFC with planning demands for the first layer of the decision tree (Navigation>Control). We found bilateral lateral PFC activity correlated with planning demands (P<0.001 uncorrected) during Detours in navigation routes, but not in control routes. We found no significant correlations when the planning demands of first and second layer combined were entered in the analysis. The statistical parametric maps are displayed on the mean structural image at a threshold of P<0.005 uncorrected and five voxels minimum cluster size. See Supplementary Table 11 for details of activations. Comparison of parameter estimates of peak voxel in the right lateral PFC showed a significantly greater response at Detours compared with Decision Points (t23=3.49, P=0.002).

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