Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold

Abstract

It takes effort and time to tame one's impulses. Although medial prefrontal cortex (mPFC) is broadly implicated in effortful control over behavior, the subthalamic nucleus (STN) is specifically thought to contribute by acting as a brake on cortico-striatal function during decision conflict, buying time until the right decision can be made. Using the drift diffusion model of decision making, we found that trial-to-trial increases in mPFC activity (EEG theta power, 4-8 Hz) were related to an increased threshold for evidence accumulation (decision threshold) as a function of conflict. Deep brain stimulation of the STN in individuals with Parkinson's disease reversed this relationship, resulting in impulsive choice. In addition, intracranial recordings of the STN area revealed increased activity (2.5-5 Hz) during these same high-conflict decisions. Activity in these slow frequency bands may reflect a neural substrate for cortico-basal ganglia communication regulating decision processes.

Figure 1

Theoretical model, task and performance. (a) Proposed model of mPFC-STN gating of decision threshold. Action plans are gated in a corticostriatal loop (dashed line). In the presence of mPFC-detected conflict, the STN inhibits behavioral output by raising the threshold required for the striatum to gate action plans. This results in conflict-varying response times (solid lines). DBS to the STN interrupts this process, resulting in a disruption of the ability of mPFC to regulate control. RT, response time. (b) Task dynamics. During training, participants learned to choose one item in each pair (termed A/B and C/D) that was reinforced more often (A/B, 100%/0%; C/D, 75%/25%). In this example, the butterfly might be A and the piano might be B. During testing, participants had to choose the better stimulus, leading to high-conflict choices for win-win (A/C) and lose-lose (B/D) as well as low-conflict choices (A/D, C/B). For example, if the cake was C in training, this would reflect a high-conflict win-win cue. (c) Study I performance data (mean ± s.e.m.). (d) Study I conflict adaptation split by accuracy (mean ± s.e.m.). Suboptimal trials were relatively speeded compared with correct trials ON (but not OFF) DBS (**P < 0.01).

Figure 2

DBS ON/OFF study: scalp EEG (FCz electrode) from the test phase split by high and low conflict. (a) Stimulus presentation and response commission were characterized by notable beta power suppression and theta power enhancement compared with baseline in both ON and OFF conditions, which were combined here. (b) Topoplots of the high-low conflict difference in standardized regression (β) weights for cue-locked theta power and response time (±0.1 std β). The FCz site is indicated on the control topoplot. (c) Standardized regression (β) weights (mean ± s.e.m.) for cue-locked theta power and response time, demonstrating that DBS reversed a natural coupling of theta band power with response time slowing on high-conflict trials.

Figure 3

DBS ON/OFF study: Bayesian posterior densities of decision thresholds estimated from the drift diffusion model (ordinates) and how they varied as a function of mPFC theta (abcissa). Peaks of the distributions reflect the most likely value of the parameter. Significance was assessed by at least 95% of the distribution being to the left or right of zero. (a) Simple effects of theta. OFF DBS, increased theta was associated with increased decision threshold for high-conflict trials, but not low-conflict trials. ON DBS, increased theta was associated with a decreased decision threshold on high-conflict trials, but not low-conflict trials. (b) Theta × conflict interaction. Increases in theta resulting from high > low conflict were associated with increases in threshold OFF DBS and in healthy controls. The opposite pattern was seen ON DBS. (c) These threshold effects are reflected by changes in response time distributions. These plots show the best fit response time distributions for optimal and suboptimal choices as a function of low and high mPFC theta/threshold in affected individuals in OFF DBS sessions. Higher theta power is associated with a reduction in the density of fast suboptimal choices and greater dispersion of optimal response time distributions, fitting with an account of increased threshold.

Figure 4

Intracranial EEG from the STN for dorsal, middle and ventral leads. Both beta suppression and theta enhancement were observed in the STN. The rightmost columns show the condition-wide differences revealed by permutation testing. High-conflict trials were characterized by a diminishment of low-frequency power across leads, greater post-cue activity in the dorsal lead and greater post-response activity across leads. The bottom row shows intracranial EEG data filtered from 2.5–4.5 Hz.