Transcranial direct current stimulation (tDCS) is a promising method for altering the function of neural systems, cognition, and behavior. Evidence is emerging that it can also influence psychiatric ...symptomatology, including major depression and schizophrenia. However, there are many open questions regarding how the method might have such an effect, and uncertainties surrounding its influence on neural activity, and human cognition and functioning. In the present critical review, we identify key priorities for future research into major depression and schizophrenia, including studies of the mechanism(s) of action of tDCS at the neuronal and systems levels, the establishment of the cognitive impact of tDCS, as well as investigations of the potential clinical efficacy of tDCS. We highlight areas of progress in each of these domains, including data that appear to favor an effect of tDCS on neural oscillations rather than spiking, and findings that tDCS administration to the prefrontal cortex during task training may be an effective way to enhance behavioral performance. Finally, we provide suggestions for further empirical study that will elucidate the impact of tDCS on brain and behavior, and may pave the way for efficacious clinical treatments for psychiatric disorders.
Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of ...psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses.
Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses.
Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise.
Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.
Gambling is a common recreational activity that becomes dysfunctional in a subset of individuals, with DSM "pathological gambling" regarded as the most severe form. During gambling, players ...experience a range of cognitive distortions that promote an overestimation of the chances of winning. Near-miss outcomes are thought to fuel these distortions. We observed previously that near misses recruited overlapping circuitry to monetary wins in a study in healthy volunteers (Clark et al., 2009). The present study sought to extend these observations in regular gamblers and relate brain responses to an index of gambling severity. Twenty regular gamblers, who varied in their involvement from recreational playing to probable pathological gambling, were scanned while performing a simplified slot machine task that delivered occasional monetary wins, as well as near-miss and full-miss nonwin outcomes. In the overall group, near-miss outcomes were associated with a significant response in the ventral striatum, which was also recruited by monetary wins. Gambling severity, measured with the South Oaks Gambling Screen, predicted a greater response in the dopaminergic midbrain to near-miss outcomes. This effect survived controlling for clinical comorbidities that were present in the regular gamblers. Gambling severity did not predict win-related responses in the midbrain or elsewhere. These results demonstrate that near-miss events during gambling recruit reward-related brain circuitry in regular players. An association with gambling severity in the midbrain suggests that near-miss outcomes may enhance dopamine transmission in disordered gambling, which extends neurobiological similarities between pathological gambling and drug addiction.
Despite significant advances in neuroscience and treatment development, no widely accepted biomarkers are available to inform diagnostics or identify preferred treatments for individuals with major ...depressive disorder.
In this critical review, the authors examine the extent to which multimodal neuroimaging techniques can identify biomarkers reflecting key pathophysiologic processes in depression and whether such biomarkers may act as predictors, moderators, and mediators of treatment response that might facilitate development of personalized treatments based on a better understanding of these processes.
The authors first highlight the most consistent findings from neuroimaging studies using different techniques in depression, including structural and functional abnormalities in two parallel neural circuits: serotonergically modulated implicit emotion regulation circuitry, centered on the amygdala and different regions in the medial prefrontal cortex; and dopaminergically modulated reward neural circuitry, centered on the ventral striatum and medial prefrontal cortex. They then describe key findings from the relatively small number of studies indicating that specific measures of regional function and, to a lesser extent, structure in these neural circuits predict treatment response in depression.
Limitations of existing studies include small sample sizes, use of only one neuroimaging modality, and a focus on identifying predictors rather than moderators and mediators of differential treatment response. By addressing these limitations and, most importantly, capitalizing on the benefits of multimodal neuroimaging, future studies can yield moderators and mediators of treatment response in depression to facilitate significant improvements in shorter- and longer-term clinical and functional outcomes.
Background The capacity of drug cues to elicit drug-seeking behavior is believed to play a fundamental role in drug dependence; yet the neurofunctional basis of human drug cue-reactivity is not fully ...understood. We performed a meta-analysis to identify brain regions that are consistently activated by presentation of drug cues. Studies involving treatment-seeking and nontreatment-seeking substance users were contrasted to determine whether there were consistent differences in the neural response to drug cues between these populations. Finally, to assess the neural basis of craving, consistency across studies in brain regions that show correlated activation with craving was assessed. Methods Appropriate studies, assessing the effect of drug-related cues or manipulations of drug craving in drug-user populations across the whole brain, were obtained via the PubMed database and literature search. Activation likelihood estimation, a method of quantitative meta-analysis that estimates convergence across experiments by modeling the spatial uncertainty of neuroimaging data, was used to identify consistent regions of activation. Results Cue-related activation was observed in the ventral striatum (across both subgroups), amygdala (in the treatment-seeking subgroup and overall), and orbitofrontal cortex (in the nontreatment-seeking subgroup and overall) but not insula cortex. Although a different pattern of frontal and temporal lobe activation between the subgroups was observed, these differences were not significant. Finally, right amygdala and left middle frontal gyrus activity were positively associated with craving. Conclusions These results substantiate the key neural substrates underlying reactivity to drug cues and drug craving.
We assessed electrophysiological activity over the medial frontal cortex (MFC) during outcome-based behavioral adjustment using a probabilistic reversal learning task. During recording, participants ...were presented two abstract visual patterns on each trial and had to select the stimulus rewarded on 80% of trials and to avoid the stimulus rewarded on 20% of trials. These contingencies were reversed frequently during the experiment. Previous EEG work has revealed feedback-locked electrophysiological responses over the MFC (feedback-related negativity; FRN), which correlate with the negative prediction error Holroyd, C. B., & Coles, M. G. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity.
679–709, 2002 and which predict outcome-based adjustment of decision values Cohen, M. X., & Ranganath, C. Reinforcement learning signals predict future decisions.
371–378, 2007. Unlike previous paradigms, our paradigm enabled us to disentangle, on the one hand, mechanisms related to the reward prediction error, derived from reinforcement learning (RL) modeling, and on the other hand, mechanisms related to explicit rule-based adjustment of actual behavior. Our results demonstrate greater FRN amplitudes with greater RL model-derived prediction errors. Conversely expected negative outcomes that preceded rule-based behavioral reversal were not accompanied by an FRN. This pattern contrasted remarkably with that of the P3 amplitude, which was significantly greater for expected negative outcomes that preceded rule-based behavioral reversal than for unexpected negative outcomes that did not precede behavioral reversal. These data suggest that the FRN reflects prediction error and associated RL-based adjustment of decision values, whereas the P3 reflects adjustment of behavior on the basis of explicit rules.
Rationale
Characterisation of drug dependence using principles from behavioural economics has provided a more detailed understanding of the disorder. Although questionnaires assessing economic demand ...for cigarettes have extended these principles to nicotine addiction, aspects of the reliability and selectivity of these questionnaires remain uncertain.
Objective
Across two experiments, we attempted to reproduce significant associations of the cigarette purchase task with nicotine dependence in a young adult population of smokers and contrasted this measure with a novel chocolate purchase task. We also examined the association between these measures and performance on a preference task, measuring preference for cigarettes and chocolate.
Methods
Questionnaire measures were used within a university setting.
Results
In experiment 1, we observed associations between nicotine dependence and measures of behavioural economic demand for cigarettes, particularly
O
max
. In experiment 2, we replicated these findings again and extended them to show that similar correlations between nicotine dependence and demand for chocolate were not observed. Moreover, the indices of demand and choices on a concurrent choice cigarette task were moderately associated with each other and independently associated with nicotine dependence.
Conclusions
The two experiments clearly supported previous findings regarding the association between nicotine dependence and economic demand for cigarettes. We extend these observations by showing that the generalisation of economic demand across different commodities is relatively weak, but that generalisation across different procedures is strong. Our results therefore support behavioural economic models of nicotine addiction which emphasise a robust proximal role for the incentive value of cigarettes.
Reinforcement learning
describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments—
prediction error
—is thought ...to update the
expected value
of actions and predictive stimuli. Electrophysiological and lesion studies have suggested that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that had employed algorithmic reinforcement learning models across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, whereas instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies.
Over the past few decades, neuroimaging research in Bipolar Disorder (BD) has identified neural differences underlying cognitive and emotional processing. However, substantial clinical and ...methodological heterogeneity present across neuroimaging experiments potentially hinders the identification of consistent neural biomarkers of BD. This meta-analysis aims to comprehensively reassess brain activation and connectivity in BD in order to identify replicable differences that converge across and within resting-state, cognitive, and emotional neuroimaging experiments.
Neuroimaging experiments (using fMRI, PET, or arterial spin labeling) reporting whole-brain results in adults with BD and controls published from December 1999-June 18, 2019 were identified via PubMed search. Coordinates showing significant activation and/or connectivity differences between BD participants and controls during resting-state, emotional, or cognitive tasks were extracted. Four parallel, independent meta-analyses were calculated using the revised activation likelihood estimation algorithm: all experiment types, all resting-state experiments, all cognitive experiments, and all emotional experiments. To confirm reliability of identified clusters, two different meta-analytic significance tests were employed.
205 published studies yielding 506 individual neuroimaging experiments (150 resting-state, 134 cognitive, 222 emotional) comprising 5745 BD and 8023 control participants were included. Five regions survived both significance tests. Individuals with BD showed functional differences in the right posterior cingulate cortex during resting-state experiments, the left amygdala during emotional experiments, including those using a mixed (positive/negative) valence manipulation, and the left superior and right inferior parietal lobules during cognitive experiments, while hyperactivating the left medial orbitofrontal cortex during cognitive experiments. Across all experiments, there was convergence in the right caudate extending to the ventral striatum, surviving only one significance test.
Our findings indicate reproducible localization of prefrontal, parietal, and limbic differences distinguishing BD from control participants that are condition-dependent, despite heterogeneity, and point towards a framework for identifying reproducible differences in BD that may guide diagnosis and treatment.
Topical corticosteroids possess numerous generics and similar-strength substitutes. Affordability can impact obtaining the medication prescribed.
To determine recent trends in topical corticosteroid ...pricing and potential for cost saving.
A retrospective cross-sectional study analyzing all prescriptions dispensed for topical corticosteroids from January 1, 2017 through December 31, 2021, using a US all-payer pharmacy-claims database and commercial coupon dataset, was performed.
Two hundred thirty-seven unique drug products (≥1 claim) were identified. Factors that predicted for higher cost (P < .05) were branded products (105% more expensive than generics) and ultrapotent class (55% more expensive than low potency) while ointments predicted for lower cost (19% less expensive than creams). Cash prices remained relatively stable, except for ultrapotent branded topical corticosteroids (63% increase). Cost savings were available for both brand-to-generic ($14.75 per unit) and generic-to-generic ($6.82 per unit) switching. Coupon prices were consistently lower than cash prices (r = 0.89).
Contracted rates through insurance plans were not included.
Topical corticosteroid prices over the past 5 years have stabilized, the exception being branded ultrapotent corticosteroids. Savings from switching among similar-strength substitutes remain significant despite price stabilization. Coupon prices mirror the hierarchy of cash prices and can help assess real-time costs.
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