Neural signatures of cognitive flexibility and reward sensitivity following nicotinic receptor stimulation in dependent smokers : a randomized trial

IMPORTANCE Withdrawal from nicotine is an important contributor to smoking relapse. Understanding how reward-based decision making is affected by abstinence and by pharmacotherapies such as nicotine replacement therapy and varenicline tartrate may aid cessation treatment. OBJECTIVE To independently assess the effects of nicotine dependence and stimulation of the nicotinic acetylcholine receptor on the ability to interpret valence information (reward sensitivity) and subsequently alter behavior as reward contingencies change (cognitive flexibility) in a probabilistic reversal learning task. DESIGN, SETTING, AND PARTICIPANTS Nicotine-dependent smokers and nonsmokers completed a probabilistic reversal learning task during acquisition of functional magnetic resonance imaging (fMRI) in a 2-drug, double-blind placebo-controlled crossover design conducted from January 21, 2009, to September 29, 2011. Smokers were abstinent from cigarette smoking for 12 hours for all sessions. In a fully Latin square fashion, participants in both groups underwent MRI twice while receiving varenicline and twice while receiving a placebo pill, wearing either a nicotine or a placebo patch. Imaging analysis was performed from June 15, 2015, to August 10, 2016. MAIN OUTCOME AND MEASURES A well-established computational model captured effects of smoking status and administration of nicotine and varenicline on probabilistic reversal learning choice behavior. Neural effects of smoking status, nicotine, and varenicline were tested for on MRI contrasts that captured reward sensitivity and cognitive flexibility. RESULTS The study included 24 nicotine-dependent smokers (12 women and 12 men; mean [SD] age, 35.8 [9.9] years) and 20 nonsmokers (10 women and 10 men; mean [SD] age, 30.4 [7.2] years). Computational modeling indicated that abstinent smokers were biased toward response shifting and that their decisions were less sensitive to the available evidence, suggesting increased impulsivity during withdrawal. These behavioral impairments were mitigated with nicotine and varenicline. Similarly, decreased mesocorticolimbic activity associated with cognitive flexibility in abstinent smokers was restored to the level of nonsmokers following stimulation of nicotinic acetylcholine receptors (familywise error-corrected P<.05). Conversely, neural signatures of decreased reward sensitivity in smokers (vs nonsmokers; familywise error-corrected P<.05) in the dorsal striatum and anterior cingulate cortex were not mitigated by nicotine or varenicline. CONCLUSIONS AND RELEVANCE There was a double dissociation between the effects of chronic nicotine dependence on neural representations of reward sensitivity and acute effects of stimulation of nicotinic acetylcholine receptors on behavioral and neural signatures of cognitive flexibility in smokers. These chronic and acute pharmacologic effects were observed in overlapping mesocorticolimbic regions, suggesting that available pharmacotherapies may alleviate deficits in the same circuitry for certain mental computations but not for others

Cerebellar rTMS disrupts predictive language processing

The human cerebellum plays an important role in language, amongst other cognitive and motor functions [1], but a unifying theoretical framework about cerebellar language function is lacking. In an established model of motor control, the cerebellum is seen as a predictive machine, making short-term estimations about the outcome of motor commands. This allows for flexible control, on-line correction, and coordination of movements [2]. The homogeneous cytoarchitecture of the cerebellar cortex suggests that similar computations occur throughout the structure, operating on different input signals and with different output targets [3]. Several authors have therefore argued that this ‘motor’ model may extend to cerebellar nonmotor functions [3–5], and that the cerebellum may support prediction in language processing [6]. However, this hypothesis has never been directly tested. Here, we used the ‘Visual World’ paradigm [7], where on-line processing of spoken sentence content can be assessed by recording the latencies of listeners' eye movements towards objects mentioned. Repetitive transcranial magnetic stimulation (rTMS) was used to disrupt function in the right cerebellum, a region implicated in language [8]. After cerebellar rTMS, listeners showed delayed eye fixations to target objects predicted by sentence content, while there was no effect on eye fixations in sentences without predictable content. The prediction deficit was absent in two control groups. Our findings support the hypothesis that computational operations performed by the cerebellum may support prediction during both motor control and language processing

data_files

Habit training task data, two-stage task data, modelfits for two stage tas

On the cerebellum and language: neurostimulation and imaging studies

Mounting evidence suggests a cerebellar role in language, but to date few efforts have been made to characterise this role. A well-accepted model of cerebellar function in motor control posits that forward model prediction is the central function of the cerebellum, and the cerebellar architecture is suggestive of a single cerebellar computation. Recent accounts of linguistic function have proposed that forward model prediction is integral to receptive and productive language. The aim of this thesis was to explore cerebellar language function in the context of prediction. In Chapter two, right cerebellar transcranial magnetic stimulation during an eye-tracking task affected a measure of online linguistic prediction. In Chapter three, the same linguistic prediction task was used in a group of cerebellar patients and control subjects. The deficit reported in Chapter two was not found in this chapter, but data-acquisition for the study is still ongoing. Chapter four describes a functional magnetic resonance imaging (fMRI) study where resting state connectivity before and after the acquisition of a new lexicon was compared. The right cerebellum was engaged in lexical learning. Chapter five reports posterolateral cerebellar and inferior frontal gyral activity related to online prediction using an event-related fMRI design where predictability is manipulated. Overall, findings are consistent with a cerebellar role in predictive language

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Supplementary Materials fil

Right lateral cerebellum represents linguistic predictability

Mounting evidence indicates that posterolateral portions of the cerebellum (right Crus I/II) contribute to language processing, but the nature of this role remains unclear. Based on a well-supported theory of cerebellar motor function, which ascribes to the cerebellum a role in short-term prediction through internal modeling, we hypothesize that right cerebellar Crus I/II supports prediction of upcoming sentence content. We tested this hypothesis using event-related fMRI in male and female human subjects by manipulating the predictability of written sentences. Our design controlled for motor planning and execution, as well as for linguistic features and working memory load; it also allowed separation of the prediction interval from the presentation of the final sentence item. In addition, three further fMRI tasks captured semantic, phonological, and orthographic processing to shed light on the nature of the information processed. As hypothesized, activity in right posterolateral cerebellum correlated with the predictability of the upcoming target word. This cerebellar region also responded to prediction error during the outcome of the trial. Further, this region was engaged in phonological, but not semantic or orthographic, processing. This is the first imaging study to demonstrate a right cerebellar contribution in language comprehension independently from motor, cognitive, and linguistic confounds. These results complement our work using other methodologies showing cerebellar engagement in linguistic prediction and suggest that internal modeling of phonological representations aids language production and comprehension

Computational investigations of learning and synchronization in cognitive control

Complex cognition requires binding together of stimulus, action, and other features, across different time scales. Several implementations of such binding have been proposed in the literature, most prominently synaptic binding (learning) and synchronization. Biologically plausible accounts of how these different types of binding interact in the human brain are still lacking. To this end, we adopt a computational approach to investigate the impact of learning and synchronization on both behavioral (reaction time, error rate) and neural (θ power) measures. We train four models varying in their ability to learn and synchronize for an extended period of time on three seminal action control paradigms varying in difficulty. Learning, but not synchronization, proved essential for behavioral improvement. Synchronization however boosts performance of difficult tasks, avoiding the computational pitfalls of catastrophic interference. At the neural level, θ power decreases with practice but increases with task difficulty. Our simulation results bring new insights in how different types of binding interact in different types of tasks, and how this is translated in both behavioral and neural metrics

Should I Sample or Should I Go? An approximately optimal model for deciding when to stop sampling information

People are often faced with decisions for which they need to sample noisy information from the environment. Sequential sampling models provide valuable insight into how people navigate such decisions, but the actual sampling process usually remains a black box. We propose a computationally light linear model that can elucidate what factors people use during this sampling process, and whether they are optimal in doing so. We simulated agents using our model on expanded judgement tasks with different error cost (Study 1) and sampling cost (Study 2) scenarios to determine the optimal strategies in each condition. We then tested human participants in these scenarios to see if they behave optimally and if our model could capture their sampling decisions. We found that our model fit human data well and that people could shift their sampling strategy in an optimal direction when the cost of making an error changed. When sampling cost was manipulated, however, we observed a non-optimal shift in sampling strategy. This study contributes novel insights into the effects of symmetrically manipulated cost, as well as the optimality and use of dynamic decision boundaries