Understanding decision-making deficits in neurological conditions: insights from models of natural action selection

Abstract

Models of natural action selection implicate fronto-striatal circuits in both motor and cognitive 'actions'. Dysfunction of these circuits leads to decision-making deficits in various populations. We review how computational models provide insights into the mechanistic basis for these deficits in Parkinson's patients and those with ventromedial frontal damage. We then consider implications of the models for understanding behaviour and cognition in attention-deficit/hyperactivity disorder (ADHD). Incorporation of cortical noradrenaline function into the model improves action selection in noisy environments and accounts for response variability in ADHD. We close with more general clinical implications.

Figure 1

(a) Striato-cortical loops including the direct (‘Go’) and indirect (‘NoGo’) pathways of the BG. The Go cells disinhibit the thalamus via GPi, facilitating the execution of an action represented in cortex. The NoGo cells have an opposing effect by increasing inhibition of the thalamus and suppressing action execution. Dopamine from the SNc excites synaptically driven Go activity via D1 receptors and inhibits NoGo activity via D2 receptors. GPi, internal segment of globus pallidus; GPe, external segment of globus pallidus; SNc, substantia nigra pars compacta; STN: subthalamic nucleus. (b) Neural network model of this circuit Frank (2005, 2006). Squares represent units, with height reflecting neural activity. The premotor cortex selects one of the four responses (R1–R4) via direct projections from the sensory input and is modulated by BG projections from thalamus. Go units are in the left half of the striatum layer; NoGo in the right half, with separate columns for the four response. In the case shown, striatum Go is stronger than NoGo for R2, inhibiting GPi, disinhibiting thalamus and facilitating R2 execution in cortex. A tonic level of dopamine is shown in SNc; a burst or dip ensues in a subsequent error feedback phase (data not shown), driving Go/NoGo learning. The STN exerts a dynamic ‘Global NoGo’ function on response execution and adaptively modulates the threshold at which actions are selected depending on the degree of cortical response conflict (Frank 2006). (c) Modelling BG interactions with orbitofrontal cortex in decision making (Frank & Claus 2006). The BG model is as in (b). In addition, medial and lateral OFC areas receive graded information about reward/punishment magnitude information from the ABL (amygdala), which have a top-down effect on responding within the striatum, and directly on premotor cortex, allowing more flexible behaviour. OFC_ctxt is a context layer that maintains recent reinforcement information in working memory and biases activity in OFC_med_lat for use in behavioural decisions.

Figure 2

(a) BG model Go and NoGo associations recorded from the striatum after learning that choosing stimulus A is rewarding on 80% of trials and choosing stimulus B is rewarding only on 20%. Parkinson's disease was simulated (Sim PD) by reducing DA input to the Striatum and medication was simulated (Sim DA Meds) by increasing tonic DA levels and reducing phasic DA dips. These qualitative patterns predicted learning biases in PD patients on and off medication (Frank et al. 2004). (b) The contributions of the STN to decision making were explored (Frank 2006). STN lesions improved PD-like symptoms in the model (not shown), but induced premature and inappropriate responding when having to choose among two positively reinforced responses (80 versus 70%). (c) The orbitofrontal cortex (OFC) is critical in the model for adaptive decision making when the magnitudes of decision outcomes (rewards and losses) is more relevant than their probability of occurrence (Frank & Claus 2006), providing a mechanistic explanation for decision-making deficits in patients with OFC damage and capturing classical irrational choice patterns in normals.

Figure 3

(a) Standard BG model with additional simulated cortical noradrenaline (NA) effects. The locus coeruleus (LC) fires phasically upon sufficient activation of premotor units and reciprocally modulates the gain of these units via simulated NA. (b) Normalized distributions for model reaction times (number of processing cycles before the BG facilitates a response). The LC phasic mode is associated with a narrow distribution of reaction times, peaking at 50 cycles. In the tonic mode (LC units tonically 50% activated), noisy activation of both competing responses leads to a bimodal distribution and overall more RT variability, potentially explaining the variability seen in ADHD. In the ‘supra-tonic’ mode, LC activity was tonically set to maximal firing rates, leading to faster RTs. (c) Per cent accuracy in the same simple choice discrimination simulated to generate RT distributions in panel (b). High accuracy is seen in the phasic LC mode, as premotor responsiveness is boosted only in the presence of a task-relevant stimulus–response association. The tonic and supra-tonic modes lead to activation of alternative noisy responses, which can get inappropriately executed if not dynamically modulated by the LC.