Cholinergic capacity mediates prefrontal engagement during challenges to attention: evidence from imaging genetics

Abstract

In rodent studies, elevated cholinergic neurotransmission in right prefrontal cortex (PFC) is essential for maintaining attentional performance, especially in challenging conditions. Apparently paralleling the rises in acetylcholine seen in rodent studies, fMRI studies in humans reveal right PFC activation at or near Brodmann's areas 9 (BA 9) increases in response to elevated attentional demand. In the present study, we leveraged human genetic variability in the cholinergic system to test the hypothesis that the cholinergic system contributes to the BA 9 response to attentional demand. Specifically, we scanned (BOLD fMRI) participants with a polymorphism of the choline transporter gene that is thought to limit choline transport capacity (Ile89Val variant of the choline transporter gene SLC5A7, rs1013940) and matched controls while they completed a task previously used to demonstrate demand-related increases in right PFC cholinergic transmission in rats and right PFC activation in humans. As hypothesized, we found that although controls showed the typical pattern of robust BA 9 responses to increased attentional demand, Ile89Val participants did not. Further, pattern analysis of activation within this region significantly predicted participant genotype. Additional exploratory pattern classification analyses suggested that Ile89Val participants differentially recruited orbitofrontal cortex and parahippocampal gyrus to maintain attentional performance to the level of controls. These results contribute to a growing body of translational research clarifying the role of cholinergic signaling in human attention and functional neural measures, and begin to outline the risk and resiliency factors associated with potentially suboptimal cholinergic function with implications for disorders characterized by cholinergic dysregulation.

Figure 1. Sustained Attention Task (SAT)

Each trial consisted of a variable duration monitoring interval followed by the presentation of a signal or nonsignal event. The signal was a gray square on a silver background and varied in duration. Signal and nonsignal events were pseudorandomized and occurred with equal frequency. Participants were cued to respond by a low frequency buzzer. Participants responded via buttonpress using one index finger for signal trials and the other index finger for nonsignal trials (left-right key assignment counterbalanced across participants). Correct responses were followed by a high frequency feedback tone; incorrect responses and omissions did not result in feedback. The distractor condition, dSAT, increased the attentional control demands of the task by adding a global, continuous visual distractor. During dSAT trials, the screen flashed from gray to black at 10 Hz. SAT, dSAT, and fixation (not pictured) trials were pseudorandomly intermixed.

Figure 2. Effect of distraction on SAT scores for controls and Ile89Val

Data shown are from 6 experimental runs. Black bars and thick outlined shapes display performance data for SAT trials without distraction; white bars and thin outlined shapes display performance data for dSAT trials with distraction (a) The distractor impaired performance (p < .001), and had an equivalent effect on performance for both groups (p = .47) There was no difference between groups in overall performance (p = .26). (b) Individual data are plotted to illustrate the low performance of a control participant (filled circle). This participant was included in all analyses (performance was within 3 SD of group mean). Removal of this participant and their Ile89Val match from analyses did not change major conclusions of the present study.

Figure 3. Controls, but not Ile89Val increase right BA 9 activation in the presence of distraction

Percent signal change was extracted from regions of interest for controls (gray bars, circles) and Ile89Val (pattern bars, triangles). Primary motor cortex was used as a control region. Percent signal change in the bar graphs (left) is reported relative to fixation baseline (M ± SEM). Individual participant data (right) is plotted as percent signal change for the index dSAT – SAT. (a) A significant group by distraction interaction (p = .003) revealed controls increased activation during dSAT relative to SAT in the functionally defined right IFG region of interest (p < .001), but Ile89Val did not (p = .18). (b) Similarly, a significant group by distraction interaction (p = .01) revealed controls increased activation in the anatomically defined right BA 9 region of interest (p = .008), but Ile89Val did not (p = .45). (c) There was no difference between groups in overall activation in primary motor cortex (p = .57) and no increase with distraction (p = .33) suggesting global differences in activation between groups or across distraction condition were not driving group by distraction interactions.

Figure 4. Patterns of activation in right BA 9 discriminate controls and Ile89Val

A binary support vector machine was used to test classification accuracy for controls (circles) vs Ile89Val (triangles) based on individual patterns of activation for the dSAT > SAT contrast within regions of interest. Scatter plots of group predictions for individual participants are displayed. (a) Classification accuracy based on the functionally defined region of interest was 76.9%, p = .01. (b) Classification accuracy based on the anatomically defined region of interest was 84.6%, p = .01. (c) Classification accuracy based on the control motor region of interest was at chance, 46.2%, p = .49.

Figure 5. Activation in right BA 9 increases in the presence of distraction

T-map for the univariate contrast dSAT (hits + CRs) > SAT (hits + CRs) is displayed for controls and Ile89Val groups combined. The activation in right inferior frontal gyrus (IFG) approximating BA 9 (MNI 48, 0, 30) replicated our previous results using this task (Berry et al., in prep.). Activation was also found in visual cortex, which may have been driven by visual stimulation caused by the flashing visual distractor. Activations are displayed on CARET slightly inflated surface representation with the t-value scale shown in the lower right.

Figure 6. Regions more discriminating of distraction condition for Ile89Val than controls

To investigate whether there were regions differentially involved in dSAT performance for Ile89Val than controls, we generated weight maps for the classification of dSAT and SAT trials for controls and Ile89Val using a binary support vector machine. Displayed are regions showing greater discrimination for dSAT vs SAT for Ile89Val than controls: [Ile89Val dSAT > SAT weight map] – [control dSAT > SAT weight map]. Weight maps are displayed on the average of each participant’s normalized structural scan, and are displayed in arbitrary units (A.U., see Methods). See also Figure S2.