Advancing workforce diversity by leveraging the Clinical and Translational Science Awards (CTSA) program.

Clinical trials continue to disproportionately underrepresent people of color. Increasing representation of diverse backgrounds among clinical research personnel has the potential to yield greater representation in clinical trials and more efficacious medical interventions by addressing medical mistrust. In 2019, North Carolina Central University (NCCU), a Historically Black College and University with a more than 80% underrepresented student population, established the Clinical Research Sciences Program with support from the Clinical and Translational Science Awards (CTSA) program at neighboring Duke University. This program was designed to increase exposure of students from diverse educational, racial, and ethnic backgrounds to the field of clinical research, with a special focus on health equity education. In the first year, the program graduated 11 students from the two-semester certificate program, eight of whom now hold positions as clinical research professionals. This article describes how leveraging the CTSA program helped NCCU build a framework for producing a highly trained, competent, and diverse workforce in clinical research responsive to the call for increased diversity in clinical trial participation.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Pyneal: Open Source Real-Time fMRI Software.

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant's ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.

Expected Reward Value and Reward Uncertainty Have Temporally Dissociable Effects on Memory Formation.

Anticipating rewards has been shown to enhance memory formation. Although substantial evidence implicates dopamine in this behavioral effect, the precise mechanisms remain ambiguous. Because dopamine nuclei have been associated with two distinct physiological signatures of reward prediction, we hypothesized two dissociable effects on memory formation. These two signatures are a phasic dopamine response immediately following a reward cue that encodes its expected value and a sustained, ramping response that has been demonstrated during high reward uncertainty [Fiorillo, C. D., Tobler, P. N., & Schultz, W. Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299, 1898-1902, 2003]. Here, we show in humans that the impact of reward anticipation on memory for an event depends on its timing relative to these physiological signatures. By manipulating reward probability (100%, 50%, or 0%) and the timing of the event to be encoded (just after the reward cue versus just before expected reward outcome), we demonstrated the predicted double dissociation: Early during reward anticipation, memory formation was improved by increased expected reward value, whereas late during reward anticipation, memory formation was enhanced by reward uncertainty. Notably, although the memory benefits of high expected reward in the early interval were consolidation dependent, the memory benefits of high uncertainty in the later interval were not. These findings support the view that expected reward benefits memory consolidation via phasic dopamine release. The novel finding of a distinct memory enhancement, temporally consistent with sustained anticipatory dopamine release, points toward new mechanisms of memory modulation by reward now ripe for further investigation.

Single session real-time fMRI neurofeedback has a lasting impact on cognitive behavioral therapy strategies.

To benefit from cognitive behavioral therapy (CBT), individuals must not only learn new skills but also strategically implement them outside of session. Here, we tested a novel technique for personalizing CBT skills and facilitating their generalization to daily life. We hypothesized that showing participants the impact of specific CBT strategies on their own brain function using real-time functional magnetic imaging (rt-fMRI) neurofeedback would increase their metacognitive awareness, help them identify effective strategies, and motivate real-world use. In a within-subjects design, participants who had completed a clinical trial of a standardized course of CBT created a personal repertoire of negative autobiographical stimuli and mood regulation strategies. From each participant's repertoire, a set of experimental and control strategies were identified; only experimental strategies were practiced in the scanner. During the rt-fMRI neurofeedback session, participants used negative stimuli and strategies from their repertoire to manipulate activation in the anterior cingulate cortex, a region implicated in emotional distress. The primary outcome measures were changes in participant ratings of strategy difficulty, efficacy, and frequency of use. As predicted, ratings for unscanned control strategies were stable across observations, whereas ratings for experimental strategies changed after neurofeedback. At follow-up one month after the session, efficacy and frequency ratings for scanned strategies were predicted by neurofeedback during the rt-fMRI session. These results suggest that rt-fMRI neurofeedback created a salient and durable learning experience for patients, extending beyond the scan session to guide and motivate CBT skill use weeks later. This metacognitive approach to neurofeedback offers a promising model for increasing clinical benefits from cognitive behavioral therapy by personalizing skills and facilitating generalization.

Cognitive Neurostimulation: Learning to Volitionally Sustain Ventral Tegmental Area Activation.

Activation of the ventral tegmental area (VTA) and mesolimbic networks is essential to motivation, performance, and learning. Humans routinely attempt to motivate themselves, with unclear efficacy or impact on VTA networks. Using fMRI, we found untrained participants' motivational strategies failed to consistently activate VTA. After real-time VTA neurofeedback training, however, participants volitionally induced VTA activation without external aids, relative to baseline, Pre-test, and control groups. VTA self-activation was accompanied by increased mesolimbic network connectivity. Among two comparison groups (no neurofeedback, false neurofeedback) and an alternate neurofeedback group (nucleus accumbens), none sustained activation in target regions of interest nor increased VTA functional connectivity. The results comprise two novel demonstrations: learning and generalization after VTA neurofeedback training and the ability to sustain VTA activation without external reward or reward cues. These findings suggest theoretical alignment of ideas about motivation and midbrain physiology and the potential for generalizable interventions to improve performance and learning.

Contributions of the hippocampus to feedback learning.

Humans learn about the world in a variety of manners, including by observation, by associating cues in the environment, and via feedback. Across species, two brain structures have been predominantly involved in these learning processes: the hippocampus--supporting learning via observation and paired association--and the striatum--critical for feedback learning. This simple dichotomy, however, has recently been challenged by reports of hippocampal engagement in feedback learning, although the role of the hippocampus is not fully understood. The purpose of this experiment was to characterize the hippocampal response during feedback learning by manipulating varying levels of memory interference. Consistent with prior reports, feedback learning recruited the striatum and midbrain. Notably, feedback learning also engaged the hippocampus. The level of activity in these regions was modulated by the degree of memory interference, such that the greatest activation occurred during the highest level of memory interference. Importantly, the accuracy of information learned via feedback correlated with hippocampal activation and was reduced by the presence of high memory interference. Taken together, these findings provide evidence of hippocampal involvement in feedback learning by demonstrating both its relevance for the accuracy of information learned via feedback and its susceptibility to interference.

Reward-related learning via multiple memory systems.

The application of a neuroeconomic approach to the study of reward-related processes has provided significant insights in our understanding of human learning and decision making. Much of this research has focused primarily on the contributions of the corticostriatal circuitry, involved in trial-and-error reward learning. As a result, less consideration has been allotted to the potential influence of different neural mechanisms such as the hippocampus or to more common ways in human society in which information is acquired and utilized to reach a decision, such as through explicit instruction rather than trial-and-error learning. This review examines the individual contributions of multiple learning and memory neural systems and their interactions during human decision making in both normal and neuropsychiatric populations. Specifically, the anatomical and functional connectivity across multiple memory systems are highlighted to suggest that probing the role of the hippocampus and its interactions with the corticostriatal circuitry via the application of model-based neuroeconomic approaches may provide novel insights into neuropsychiatric populations that suffer from damage to one of these structures and as a consequence have deficits in learning, memory, or decision making.

Parallel contributions of distinct human memory systems during probabilistic learning.

Regions within the medial temporal lobe and basal ganglia are thought to subserve distinct memory systems underlying declarative and nondeclarative processes, respectively. One question of interest is how these multiple memory systems interact during learning to contribute to goal directed behavior. While some hypotheses suggest that regions such as the striatum and the hippocampus interact in a competitive manner, alternative views posit that these structures may operate in a parallel manner to facilitate learning. In the current experiment, we probed the functional connectivity between regions in the striatum and hippocampus in the human brain during an event related probabilistic learning task that varied with respect to type of difficulty (easy or hard cues) and type of learning (via feedback or observation). We hypothesized that the hippocampus and striatum would interact in a parallel manner during learning. We identified regions of interest (ROI) in the striatum and hippocampus that showed an effect of cue difficulty during learning and found that such ROIs displayed a similar pattern of blood oxygen level dependent (BOLD) responses, irrespective of learning type, and were functionally correlated as assessed by a Granger causality analysis. Given the connectivity of both structures with dopaminergic midbrain centers, we further applied a reinforcement learning algorithm often used to highlight the role of dopamine in human reward related learning paradigms. Activity in both the striatum and hippocampus positively correlated with a prediction error signal during feedback learning. These results suggest that distinct human memory systems operate in parallel during probabilistic learning, and may act synergistically particularly when a violation of expectation occurs, to jointly contribute to learning and decision making.