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, 30 (9), 3210-9

Striatal Prediction Error Modulates Cortical Coupling

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Striatal Prediction Error Modulates Cortical Coupling

Hanneke E M den Ouden et al. J Neurosci.

Abstract

Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.

Figures

Figure 1.
Figure 1.
A, Timeline for a single trial. At trial onset, the auditory cue stimulus (CS) was presented for 300 ms. The visual outcome stimulus (OS) lasted for 150 ms and was presented 150 ± 50 ms after the CS. The intertrial interval lasted for 2000 ± 650 ms on average. B, Temporal evolution of the probability of a face occurring, p(F), given either CS. Note that the probability of a house being presented is simply the mirror image of this sequence. C, The posterior mean of p(F|CS) as estimated by the Bayesian learner (dashed line) tracks the underlying blocked probabilities (solid line). Note that the blocked probabilities are CS2 zoomed in from B (trials 400–600, session 3). Because blocks of stable probabilities are short, however, the estimated probabilities never quite reach their true values during a given block. Note that the estimates change rapidly at block transitions. When an unexpected stimulus occurs, the estimates briefly move toward p = 0.5. Note that, for clarity, we only show a single session (session 3) here.
Figure 2.
Figure 2.
RTs (A) and percentage of errors as a function of outcome probability (B) (mean ± SE). Correct trials were averaged within each level of probability and collapsed across CS and visual outcome type (F/H). Subjects speed up and make fewer errors the higher the probability of the outcome. C, Subject-specific differences in log model evidence (LME) for using the trial-by-trial probability estimates from the Bayesian model versus the true probabilities as linear predictors for behavioral measured response speeds. In all but two subjects, there is far greater evidence for the Bayesian model. D, The Dirichlet density describing the probability of model m1 (based on the probability estimates from the Bayesian learning model) relative to the alternative model m2 (based on the true, blocked probabilities), given the measured response speeds across the group. The shaded area represents the exceedance probability of m1 being a more likely model than m2. This exceedance probability of ϕ1 = 100.0% was strongly favoring m1 as a more likely model than m2.
Figure 3.
Figure 3.
All parameter estimates show mean ± SE across all subjects, and all activations are displayed on the average anatomical scan. B and D–F show the results of a whole-brain analysis, and A, C, G, and H show the results from region-of-interest analyses. A, Effect of prediction error in the anterior putamen bilaterally. C, Parameter estimates from the putamen showing the negative dependency on both p(F) and the p(H). B, Effects of prediction error in PMd and the parietal cortex. a.u., Arbitrary units. D, Parameter estimates for the left PMd, showing the same prediction error-dependent effect as the putamen. E, Main effect of F > H in the right FFA, also showing the left FFA activation (see supplemental data, available at www.jneurosci.org as supplemental material). F, Main effect of H > F in the bilateral PPA. G, Parameter estimates of the modulatory effect of stimulus probabilities (from the individual maxima for the orthogonal F > H contrast in the FFA). There was a pronounced negative modulation of FFA responses to faces by the trial-by-trial probability estimates for faces (β = −2.05 ± 0.52). In contrast, the modulation of FFA responses to houses by the trial-by-trial probability estimates for houses was marginal (β = −0.09 ± 0.78). This difference was significant (*p = 0.037). H, Parameter estimates of the modulatory effect of stimulus probabilities across subjects (from the individual maxima for the orthogonal H > F contrast in the PPA). PPA responses to houses showed a strongly negative modulation by the trial-by-trial probability estimates for houses (β = −2.29 ± 0.54). In contrast, PPA responses to faces were positively modulated by the trial-by-trial probability estimates for faces (β = 1.91 ± 0.67). This difference was significant (*p = 0.00005).
Figure 4.
Figure 4.
A, A basic DCM for investigating modulation of visuomotor connections by prediction error-related activity in the putamen. B, The optimal DCM (model mpt), resulting from a systematic model search procedure, included full connectivity between the PMd, PPA, and FFA. Activity in the putamen significantly enhanced the connections from the PPA/FFA to the premotor cortex (p = 0.010 and p = 0.017, respectively). C, Alternative DCM (model mpm) in which the roles of the putamen and the PMd were swapped. D, The Dirichlet density describing the probability of the “putamen ” model mpt relative to the alternative “premotor ” model mpm, given the measured fMRI data across the group. The shaded area represents the exceedance probability of mpt being a more likely model than mpm. This exceedance probability of ϕ1 = 99.1% was strongly favoring mpt as a more likely model than mpm.

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