Developmental changes in effective connectivity associated with relational reasoning

Abstract

Rostrolateral prefrontal cortex (RLPFC) is part of a frontoparietal network of regions involved in relational reasoning, the mental process of working with relationships between multiple mental representations. RLPFC has shown functional and structural changes with age, with increasing specificity of left RLPFC activation for relational integration during development. Here, we used dynamic causal modeling (DCM) to investigate changes in effective connectivity during a relational reasoning task through the transition from adolescence into adulthood. We examined fMRI data of 37 healthy female participants (11–30 years old) performing a relational reasoning paradigm. Comparing relational integration to the manipulation of single relations revealed activation in five regions: the RLPFC, anterior insula, dorsolateral PFC, inferior parietal lobe, and medial superior frontal gyrus. We used a new exhaustive search approach and identified a full DCM model, which included all reciprocal connections between the five clusters in the left hemisphere, as the optimal model. In line with previous resting state fMRI results, we showed distinct developmental effects on the strength of long-range frontoparietal versus frontoinsular short-range fixed connections. The modulatory connections associated with relational integration increased with age. Gray matter volume in left RLPFC, which decreased with age, partly accounted for changes in fixed PFC connectivity. Finally, improvements in relational integration performance were associated with greater modulatory and weaker fixed PFC connectivity. This pattern provides further evidence of increasing specificity of left PFC function for relational integration compared to the manipulation of single relations, and demonstrates an association between effective connectivity and performance during development.

Figure 1

Stimuli of the experimental paradigm. In the Control condition (left), participants were asked whether one of the items in the first pair of items (top row) had the same shape (or texture) as the second pair of items (bottom row). In this example, the top left item has the same shape (circle) as the bottom items, thus the answer is yes. In the Relational condition (on the right), participants were asked whether the two pairs changed along the same dimension (shape or texture). Here both pairs change along the shape dimension, so the answer is yes.

Figure 2

Main effect of Relational > Control. The main effect of the experimental condition (Relational > Control) revealed activation in the RLPFC, DLPFC, AI, mSFG, and IPL across all participants. Only activations in the left hemisphere used in the DCM analyses are shown here. Top row: horizontal slices ranging from z = −8 to z = 60. Bottom row: lateral and medial view, lateral view with a cut‐off at x = −32, lateral frontal view with a cut‐off at y = 22, z = 2. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 3

Results of the post‐hoc model search. (A) The full DCM, as illustrated here, had the highest probability compared with all other possible models. (B and C) Mean (± SE) connection probabilities for the fixed (PpA) and modulatory (PpB) connections. Probabilities were above chance (>50%) for all connections. (D and E) Mean (± SE) exponentiated parameter estimates for fixed (EpA) and modulatory (EpB) connections are also shown. Probability and parameter estimate values for self‐connections are not included for simplicity.

Figure 4

Parameter estimates of the fixed and modulatory short‐range and long‐range connections plotted for each age group. (A) Exponentiated mean and 95% confidence interval (CI) of the parameter estimates of fixed connections (EpA). Long‐range connections were stronger than short‐range connections, and there was a significant interaction between Connection length and Age group. Short‐range connections were stronger in young adolescents than mid adolescents and adults (indicated by asterisk), while long‐range connections did not differ between age groups. (B) Exponentiated mean and 95% CI of the parameter estimates of the modulatory connections. Long‐range connections were again stronger than short‐range connections. Connectivity strength was also greater in adults than in young adolescents (indicated by asterisk).

Figure 5

Scatter plots of left RLPFC adjusted gray matter volume as a function of age, and the parameter estimates of frontal short‐range fixed connections (EpA) as a function of age and RLPFC structure. (A) Left RLPFC adjusted gray matter volume plotted as a function of age. (B) Frontal short‐range exponentiated EpA plotted as a function of adjusted gray matter volume in the left RLPFC. (C) Frontal short‐range exponentiated EpA plotted as a function of age. Both measures (i.e., RLPFC structure and age) predicted fixed short‐range connection strength, but no significant mediation was observed. Note that statistics were performed on non‐exponentiated data and the fit line is shown here for illustration purposes.

Figure 6

Scatter plot of Relational versus Control accuracy as a function of frontal fixed (EpA) and modulatory (EpB) short‐range connections. (A) The difference in accuracy between Control and Relational trials (a greater positive value means poorer performance in Relational than Control trials) was positively predicted by the strength of fixed connections. (B) The difference in accuracy between Control and Relational trials was negatively predicted by the strength of modulatory connections. Thus, overall weaker fixed connections and stronger modulatory connections predicted a smaller difference between Relational and Control accuracy, i.e. a relatively better performance in Relational trials. Note that statistics were performed on non‐exponentiated data and the fit line is shown here for illustration purposes.