Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques

One of the most influential accounts of central orbitofrontal cortex-that it mediates behavioral flexibility-has been challenged by the finding that discrimination reversal in macaques, the classic test of behavioral flexibility, is unaffected when lesions are made by excitotoxin injection rather than aspiration. This suggests that the critical brain circuit mediating behavioral flexibility in reversal tasks lies beyond the central orbitofrontal cortex. To determine its identity, a group of nine macaques were taught discrimination reversal learning tasks, and its impact on gray matter was measured. Magnetic resonance imaging scans were taken before and after learning and compared with scans from two control groups, each comprising 10 animals. One control group learned discrimination tasks that were similar but lacked any reversal component, and the other control group engaged in no learning. Gray matter changes were prominent in posterior orbitofrontal cortex/anterior insula but were also found in three other frontal cortical regions: lateral orbitofrontal cortex (orbital part of area 12 [12o]), cingulate cortex, and lateral prefrontal cortex. In a second analysis, neural activity in posterior orbitofrontal cortex/anterior insula was measured at rest, and its pattern of coupling with the other frontal cortical regions was assessed. Activity coupling increased significantly in the reversal learning group in comparison with controls. In a final set of experiments, we used similar structural imaging procedures and analyses to demonstrate that aspiration lesion of central orbitofrontal cortex, of the type known to affect discrimination learning, affected structure and activity in the same frontal cortical circuit. The results identify a distributed frontal cortical circuit associated with behavioral flexibility

The representation of priors and decisions in the human parietal cortex.

Animals actively sample their environment through orienting actions such as saccadic eye movements. Saccadic targets are selected based both on sensory evidence immediately preceding the saccade, and a "salience map" or prior built-up over multiple saccades. In the primate cortex, the selection of each individual saccade depends on competition between target-selective cells that ramp up their firing rate to saccade release. However, it is less clear how a cross-saccade prior might be implemented, either in neural firing or through an activity-silent mechanism such as modification of synaptic weights on sensory inputs. Here, we present evidence from magnetoencephalography for 2 distinct processes underlying the selection of the current saccade, and the representation of the prior, in human parietal cortex. While the classic ramping decision process for each saccade was reflected in neural firing rates (measured in the event-related field), a prior built-up over multiple saccades was implemented via modulation of the gain on sensory inputs from the preferred target, as evidenced by rapid frequency tagging. A cascade of computations over time (initial representation of the prior, followed by evidence accumulation and then an integration of prior and evidence) provides a mechanism by which a salience map may be built up across saccades in parietal cortex. It also provides insight into the apparent contradiction that inactivation of parietal cortex has been shown not to affect performance on single-trials, despite the presence of clear evidence accumulation signals in this region

Data from: Control of entropy in neural models of environmental state

Humans and animals construct internal models of their environment in order to select appropriate courses of action. The representation of uncertainty about the current state of the environment is a key feature of these models that controls the rate of learning as well as directly affecting choice behaviour. To maintain flexibility, given that uncertainty naturally decreases over time, most theoretical inference models include a dedicated mechanism to drive up model uncertainty. Here we probe the long-standing hypothesis that noradrenaline is involved in determining the entropy, and thus flexibility, of neural models. Pupil diameter, which indexes neuromodulatory state including noradrenaline release, predicted increases (but not decreases) in entropy in a neural state model encoded in human medial orbitofrontal cortex, as measured using multivariate functional MRI. Activity in anterior cingulate cortex predicted pupil diameter. These results provide evidence for top-down, neuromodulatory control of entropy in neural state models

LPNec : laboratório de psicologia experimental, neurociências e comportamento

<div><p>A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI) we showed that activity in two brain systems (typically associated with reward learning and motor control) could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.</p></div

The prediction task.

<p>(A) On each trial, participants see a target “space invader” moving down the screen. The target appears at a series of locations in rapid succession (shown here as dots, simultaneously, for illustration) to give the impression of motion. The bottom part of the trajectory is occluded (grey box). Participants must predict where the trajectory will emerge from the occluder (trajectory endpoint). They indicate their response by moving a cursor; after they finalise their response by a button-press, feedback is given, as the target appears at its true endpoint. Trajectories are parabolic, but the start point and curvature are changed randomly on each trial. (B) The participant's estimate of the trajectory was modelled as a quadratic curve. The “best estimate” trajectory is shown here as a solid blue line; the regions indicated by the three levels of blue shading indicate the range of trajectories falling within 1, 2, and 3 standard errors from the best estimated trajectory. (C) This results in a Gaussian probabilistic estimate of the trajectory endpoint (blue bell curve). (D) The trajectory endpoints over many trials (represented by the red histogram) follow a Gaussian distribution (red bell curve), which gives some statistical information a priori about where the endpoint will be. This information can be used to reduce uncertainty in noisy trajectories. The mean and variance of this underlying distribution change periodically and must be learned using a statistical model.</p

Three alternative ways of combining the trajectory with the statistical model of the environment.

<p>Illustration of the three models we compared. In each case a statistical model of the environment over many trials (red) and trajectory estimate (blue) are combined. (a) Weighted combination model—the response is based on the precision-weighted combined distribution (purple). (b) Unweighted combination model—the response in between the predictions from the two models, but does not depend on their relative precision. (c) Weighted noncombination—the actor chooses the prediction with the highest precision but does not combine information from the two predictions.</p

Brain regions associated with the dynamic forward model.

<p>Activity correlated with the precision of prediction from the dynamic forward model. Cortical activity and subcortical activity in cerebellum and caudate. The figure shows group Z-maps for the 22 participants, thresholded at <i>p</i><0.05 corrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#s4" target="_blank">Methods</a>). A full table of activation peaks is given in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#pbio.1001662.s005" target="_blank">Table S2</a>.</p

Summary of the characteristics of two classes of predictive model.

<p>The equations are typical instantiations of each model class—for the statistical endpoints distribution model, a temporal difference learning rule in which the value of an item on iteration (V_t+1) is equal to the value on iteration t, plus some proportion of the prediction error (δ) times learning rate (α). Dynamic forward models would typically be captured by a set of differential equations, where the rate of change of some parameters (Θ), such as position, is a function of time, f(t).</p

Activity associated with the statistical model and with accuracy.

<p>(A) Activity correlated with the precision of prediction from the statistical model. The figure shows group Z-maps for the 22 participants, thresholded at <i>p</i><0.05 corrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#s4" target="_blank">Methods</a>). (B) Parameter estimates for the effect of precision of the statistical model, precision of the trajectory estimate, and trial-to-trial accuracy, for a region of interest in the orbitofrontal cortex, defined based on a meta-analysis <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#pbio.1001662-Kringelbach1" target="_blank">[65]</a>. Bars show group mean, and error bars show s.e.m. Note that although there is a significant effect of precision for the statistical model (<i>p</i> = 0.036, one sample <i>t</i> test against zero), there is no effect of accuracy per se (<i>p</i> = 0.82) or of the precision of the dynamic model (<i>p</i> = 0.55); note, in the region of interest analysis, accuracy is not orthogonalised with respect to model precisions, so the effects of model precision are independent of variance that could also be explained by overall accuracy. This is why effect sizes look slightly different to in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#pbio-1001662-g006" target="_blank">Figure 6</a>. (C) Activity relating to trial-to-trial accuracy. The figure shows group Z-maps for the 22 participants, thresholded at <i>p</i><0.05 corrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#s4" target="_blank">Methods</a>). Note the strong peak in the ventral striatum. Slice location is y = 6, peak effect at 20, 6, −10, Z = 4.8. In the whole brain analysis, accuracy was orthogonalised with respect to the model precisions, with which it was correlated (as in panel E). (D) Parameter estimates as in (B), but for a region of interest in the ventral striatum, defined using the nucleus accumbens mask from the Harvard-Oxford atlas, available in FSL (<a href="http://www.fmrib.ox.ac.uk/fsl" target="_blank">www.fmrib.ox.ac.uk/fsl</a>). Note that this ROI is strongly affected by overall accuracy (<i>p</i> = 0.0015, one sample <i>t</i> test against zero) but not by the precision of the statistical (<i>p</i> = 0.23) or dynamic (<i>p</i> = 0.61) models. (E) Behavioral effects of precision of the statistical model and trajectory estimate on accuracy. Bars show group mean ± s.e.m. effect size from a multiple regression of accuracy on precisions for the two models. The effect of precision for both the statistical and dynamic models were significant (<i>t</i> test versus zero, <i>p</i><0.01 and <i>p</i><0.0001, respectively), but the effect of dynamic model precision was much greater (paired <i>t</i> test, <i>p</i><0.0001).</p