Despite growing interest in multi-voxel pattern analysis (MVPA) methods for fMRI, a major problem remains—that of generating estimates in rapid event-related (ER) designs, where the BOLD responses of temporally adjacent events will overlap. While this problem has been investigated for methods that reduce each event to a single parameter per voxel (Mumford et al., 2012), most of these methods make strong parametric assumptions about the shape of the hemodynamic response, and require exact knowledge of the temporal profile of the underlying neural activity. A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully. In addition to enabling a more accurate estimate of ER responses, this allows for the extension of the standard classification paradigm into the temporal domain (e.g., Mourão-Miranda et al., 2007). However, existing methods in this class were developed for use with block and slow ER data, and there has not yet been an exploration of how to adapt such methods to data collected using rapid ER designs. Here, we demonstrate that the use of multiple parameters preserves or improves classification accuracy, while additionally providing information on the evolution of class discrimination. Additionally, we explore an alternative to the method of Mourão-Miranda et al. tailored to use in rapid ER designs that yields equivalent classification accuracies, but is better at unmixing responses to temporally adjacent events. The current work paves the way for wider adoption of spatiotemporal classification analyses, and greater use of MVPA with rapid ER designs.
Functional magnetic resonance imaging; Classification analysis; MVPA; Rapid event-related design
Higher levels of impulsivity have been implicated in the development of alcohol use disorders. Recent findings suggest that impulsivity is not a unitary construct, highlighted by the diverse ways in which the various measures of impulsivity relate to alcohol use outcomes. This study simultaneously tested the following dimensions of impulsivity as determinants of alcohol use and alcohol problems: risky decision-making, self-reported risk attitudes, response inhibition, and impulsive decision-making.
Participants were a community sample of non-treatment seeking problem drinkers (N = 158). Structural Equation Modeling (SEM) analyses employed behavioral measures of impulsive decision-making (Delay Discounting Task, DDT), response inhibition (Stop Signal Task, SST), and risky decision-making (Balloon Analogue Risk Task, BART), and a self-report measure of risk attitudes (Domain-specific Risk-attitude Scale, DOSPERT), as predictors of alcohol use and of alcohol-related problems in this sample.
The model fit well, accounting for 38% of the variance in alcohol problems, and identified two impulsivity dimensions that significantly loaded onto alcohol outcomes: (1) impulsive decision-making, indexed by the DDT; and (2) risky decision-making, measured by the BART.
The impulsive decision-making dimension of impulsivity, indexed by the DDT, was the strongest predictor of alcohol use and alcohol pathology in this sample of problem drinkers. Unexpectedly, a negative relationship was found between risky decision-making and alcohol problems. The results highlight the importance of considering the distinct facets of impulsivity in order to elucidate their individual and combined effects on alcohol use initiation, escalation, and dependence.
impulsivity; alcohol use; alcohol problems; delayed reward discounting; risk-taking
Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA) is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are, but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel’s membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results.
Functional Magnetic Resonance Imaging; Functional Connectivity; Graph Theory; Small World Networks
Use of multivoxel pattern analysis (MVPA) to predict the cognitive state of a subject during task performance has become a popular focus of fMRI studies. The input to these analyses consists of activation patterns corresponding to different tasks or stimulus types. These activation patterns are fairly straightforward to calculate for blocked trials or slow event-related designs, but for rapid event-related designs the evoked BOLD signal for adjacent trials will overlap in time, complicating the identification of signal unique to specific trials. Rapid event-related designs are often preferred because they allow for more stimuli to be presented and subjects tend to be more focused on the task, and thus it would be beneficial to be able to use these types of designs in MVPA analyses. The present work compares 8 different models for estimating trial-by-trial activation patterns for a range of rapid event-related designs varying by interstimulus interval and signal-to-noise ratio. The most effective approach obtains each trial’s estimate through a general linear model including a regressor for that trial as well as another regressor for all other trials. Through the analysis of both simulated and real data we have found that this model shows some improvement over the standard approaches for obtaining activation patterns. The resulting trial-by-trial estimates are more representative of the true activation magnitudes, leading to a boost in classification accuracy in fast event-related designs with higher signal-to-noise. This provides the potential for fMRI studies that allow simultaneous optimization of both univariate and MVPA approaches.
Functional Magnetic Resonance Imaging; Classification Analysis; MVPA; Beta Series Estimation; Rapid Event-Related Design
The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.
informatics; data sharing; metadata; multivariate; classification
Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART), which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analog Risk Taking (BART) task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity (DI) and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step toward characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.
functional impulsivity; dysfunctional impulsivity; risky decision-making; naturalistic risk-taking; ART; BART
A common goal of neuroimaging research is to use imaging data to identify the mental processes that are engaged when a subject performs a mental task. The use of reasoning from activation to mental functions, known as “reverse inference”, has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question. In this Perspective, I outline the critique of informal reverse inference, and describe a number of new developments that provide the ability to more formally test the predictive power of neuroimaging data.
Impulsive behavior is thought to reflect a trait-like characteristic that can have broad consequences for an individual’s success and well-being, but its neurobiological basis remains elusive. Although striatal dopamine D2-like receptors have been linked with impulsive behavior and behavioral inhibition in rodents, a role for D2-like receptor function in frontostriatal circuits mediating inhibitory control in humans has not been shown. We investigated this role in a study of healthy research participants who underwent positron emission tomography with the D2/D3 dopamine-receptor ligand [18F]fallypride, and blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) while they performed the Stop-signal Task, a test of response inhibition. Striatal dopamine D2/D3-receptor availability was negatively correlated with speed of response inhibition (stop-signal reaction time), and positively correlated with inhibition-related fMRI activation in frontostriatal neural circuitry. Correlations involving D2/D3 receptor availability were strongest in the dorsal regions (caudate and putamen) of the striatum, consistent with findings of animal studies relating dopamine receptors and response inhibition. The results suggest that striatal D2-like receptor function in humans plays a major role in the neural circuitry that mediates behavioral control, an ability that is essential for adaptive responding and is compromised in a variety of common neuropsychiatric disorders.
Response Inhibition; fMRI; Stop-signal Task; Dopamine; PET
Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.
One of the major challenges of neuroscience research is to integrate the results of the large number of published research studies in order to better understand how psychological functions are mapped onto brain systems. In this research, we take advantage of a large database of neuroimaging studies, along with text mining methods, to extract information about the topics that are found in the brain imaging literature and their mapping onto reported brain activation data. We also show that this method can be used to identify new relations between psychological functions and mental disorders, through their shared brain activity patterns. This work provides a new way to discover the underlying structure that relates brain function and mental processes.
The vertex sharp transient (VST) is an electroencephalographic (EEG) discharge that is an early marker of non-REM sleep. It has been recognized since the beginning of sleep physiology research, but its source and function remain mostly unexplained. We investigated VST generation using functional MRI (fMRI).
Simultaneous EEG and fMRI were recorded from 7 individuals in drowsiness and light sleep. VST occurrences on EEG were modeled with fMRI using an impulse function convolved with a hemodynamic response function to identify cerebral regions correlating to the VSTs. A resulting statistical image was thresholded at Z>2.3.
Two hundred VSTs were identified. Significantly increased signal was present bilaterally in medial central, lateral precentral, posterior superior temporal, and medial occipital cortex. No regions of decreased signal were present.
The regions are consistent with electrophysiologic evidence from animal models and functional imaging of human sleep, but the results are specific to VSTs. The regions principally encompass the primary sensorimotor cortical regions for vision, hearing, and touch.
The results depict a network comprising the presumed VST generator and its associated regions. The associated regions functional similarity for primary sensation suggests a role for VSTs in sensory experience during sleep.
electroencephalography (EEG); functional MRI (fMRI); sleep; vertex sharp transients
Methamphetamine (MA)-dependent individuals exhibit deficits in cognition and prefrontal cortical function. Therefore, medications that improve cognition in these subjects may improve the success of therapy for their addiction, especially when cognitive behavioral therapies are used. Modafinil has been shown to improve cognitive performance in neuropsychiatric patients and healthy volunteers. We therefore conducted a randomized, double-blind, placebo-controlled, cross-over study, using functional magnetic resonance imaging, to examine the effects of modafinil on learning and neural activity related to cognitive function in abstinent, MA-dependent, and healthy control participants. Modafinil (200 mg) and placebo were administered orally (one single dose each), in counterbalanced fashion, 2 h before each of two testing sessions. Under placebo conditions, MA-dependent participants showed worse learning performance than control participants. Modafinil boosted learning in MA-dependent participants, bringing them to the same performance level as control subjects; the control group did not show changes in performance with modafinil. After controlling for performance differences, MA-dependent participants showed a greater effect of modafinil on brain activation in bilateral insula/ventrolateral prefrontal cortex and anterior cingulate cortices than control participants. The findings suggest that modafinil improves learning in MA-dependent participants, possibly by enhancing neural function in regions important for learning and cognitive control. These results suggest that modafinil may be a suitable pharmacological adjunct for enhancing the efficiency of cognitive-based therapies for MA dependence.
modafinil; fMRI; methamphetamine; anterior cingulate; learning; drug abuse; addiction & substance abuse; learning & memory; imaging, clinical or preclinical; psychopharmacology; modafinil; fMRI; methamphetamine; anterior cingulate; learning
Smoking is usually initiated in adolescence, and is the leading preventable cause of death in the United States. Little is known, however, about the links between smoking and neurobiological function in adolescent smokers. This study aimed to probe prefrontal cortical function in late adolescent smokers, using a response inhibition task, and to assess possible relationships between inhibition-related brain activity, clinical features of smoking behavior, and exposure to cigarette smoking. Participants in this study were otherwise healthy late adolescent smokers (15–21 years of age; n=25), who reported daily smoking for at least the 6 months before testing, and age- and education-matched nonsmokers (16–21 years of age; n=25), who each reported smoking fewer than five cigarettes in their lifetimes. The subjects performed the Stop-signal Task, while undergoing functional magnetic resonance imaging. There were no significant group differences in prefrontal cortical activity during response inhibition, but the Heaviness of Smoking Index, a measure of smoking behavior and dependence, was negatively related to neural function in cortical regions of the smokers. These findings suggest that smoking can modulate prefrontal cortical function. Given the late development of the prefrontal cortex, which continues through adolescence, it is possible that smoking may influence the trajectory of brain development during this critical developmental period.
adolescence; smoking; fMRI; inhibitory control; brain development; Development/Developmental Disorders; Addiction & Substance Abuse; Imaging; Clinical or Preclinical; Biological Psychiatry; fMRI; adolescence; brain development; inhibitory control; smoking
Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half under the massed learning condition (i.e., four consecutive repetitions with jittered interstimulus interval) and the other half under the spaced learning condition (i.e., the four repetitions were interleaved). Recognition memory tests afterward revealed a significant spacing effect: Participants recognized more items learned under the spaced learning condition than under the massed learning condition. Successful face memory encoding was associated with stronger activation in the bilateral fusiform gyrus, which showed a significant repetition suppression effect modulated by subsequent memory status and spaced learning. Specifically, remembered faces showed smaller repetition suppression than forgotten faces under both learning conditions, and spaced learning significantly reduced repetition suppression. These results suggest that spaced learning enhances recognition memory by reducing neural repetition suppression.
Response inhibition plays a critical role in adaptive functioning and can be assessed with the Stop-signal task, which requires participants to suppress prepotent motor responses. Evidence suggests that this ability to inhibit a prepotent motor response (reflected as Stop-signal reaction time (SSRT)) is a quantitative and heritable measure of interindividual variation in brain function. Although attention has been given to the optimal method of SSRT estimation, and initial evidence exists in support of its reliability, there is still variability in how Stop-signal task data are treated across samples. In order to examine this issue, we pooled data across three separate studies and examined the influence of multiple SSRT calculation methods and outlier calling on reliability (using Intra-class correlation). Our results suggest that an approach which uses the average of all available sessions, all trials of each session, and excludes outliers based on predetermined lenient criteria yields reliable SSRT estimates, while not excluding too many participants. Our findings further support the reliability of SSRT, which is commonly used as an index of inhibitory control, and provide support for its continued use as a neurocognitive phenotype.
response inhibition; stop-signal reaction time; reliability
The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate an automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature, and support accurate ‘decoding’ of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
Economists define risk in terms of variability of possible outcomes whereas clinicians and laypeople generally view risk as exposure to possible loss or harm. Neuroeconomic studies using relatively simple behavioral tasks have identified a network of brain regions that respond to economic risk, but these studies have had limited success predicting naturalistic risk-taking. In contrast, more complex behavioral tasks developed by clinicians (e.g., Balloon Analogue Risk Task and Iowa Gambling Task) correlate with naturalistic risk-taking but resist decomposition into distinct cognitive constructs. We propose that to bridge this gap and better understand neural substrates of naturalistic risk-taking, new tasks are needed that: (1) are decomposable into basic cognitive/economic constructs; (2) predict naturalistic risk-taking; and (3) engender dynamic, affective engagement.
Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.
brain imaging; data sharing; standards; magnetic resonance imaging; fMRI; EEG-MEG
Functional imaging studies examining the neural correlates of risk have mainly relied on paradigms involving exposure to simple chance gambles and an economic definition of risk as variance in the probability distribution over possible outcomes. However, there is little evidence that choices made during gambling tasks predict naturalistic risk-taking behaviors such as drug use, extreme sports, or even equity investing. To better understand the neural basis of naturalistic risk-taking, we scanned participants using fMRI while they completed the Balloon Analog Risk Task, an experimental measure that includes an active decision/choice component and that has been found to correlate with a number of naturalistic risk-taking behaviors. In the task, as in many naturalistic settings, escalating risk-taking occurs under uncertainty and might be experienced either as the accumulation of greater potential rewards, or as exposure to increasing possible losses (and decreasing expected value). We found that areas previously linked to risk and risk-taking (bilateral anterior insula, anterior cingulate cortex, and right dorsolateral prefrontal cortex) were activated as participants continued to inflate balloons. Interestingly, we found that ventromedial prefrontal cortex (vmPFC) activity decreased as participants further expanded balloons. In light of previous findings implicating the vmPFC in value calculation, this result suggests that escalating risk-taking in the task might be perceived as exposure to increasing possible losses (and decreasing expected value) rather than the increasing potential total reward relative to the starting point of the trial. A better understanding of how neural activity changes with risk-taking behavior in the task offers insight into the potential neural mechanisms driving naturalistic risk-taking.
risk; risk-taking; BART; ventromedial prefrontal cortex; decision-making; fMRI
The ability to form associations between previously unrelated items of information, such as names and faces, is an essential aspect of episodic memory function. The neural substrate that determines success vs. failure in learning these associations remains to be elucidated. Using event-related functional MRI during the encoding of novel face-name associations, we found that successfully remembered face-name pairs showed significantly greater activation in the anterior hippocampal formation bilaterally and left inferior prefrontal cortex, compared to pairs that were forgotten. Functional connectivity analyses revealed significant correlated activity between the right and left hippocampus and neocortical regions during successful, but not attempted, encoding. These findings suggest that anterior regions of the hippocampal formation, in particular, are crucial for successful associative encoding and that the degree of coordination between hippocampal and neocortical activity may predict the likelihood of subsequent memory.
Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examing the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of vox-elwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent components analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.
response inhibition; Stop-signal; independent components analysis; fMRI; individual differences
Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.
Psychological and neurocognitive studies have suggested that different kinds of self-control may share a common psychobiological component. If this is true, performance in affective and non-affective inhibitory control tasks in the same individuals should be correlated and should rely upon integrity of this region. To test this hypothesis, we acquired high-resolution magnetic resonance images from 44 healthy and 43 methamphetamine-dependent subjects. Individuals with methamphetamine-dependence were tested because of prior findings that they suffer inhibitory control deficits. Gray matter structure of the inferior frontal gyrus was assessed using voxel-based morphometry. Subjects participated in tests of motor and affective inhibitory control (stop-signal task and emotion reappraisal task, respectively); and methamphetamine-dependent subjects provided self-reports of their craving for methamphetamine. Performance levels on the two inhibitory control tasks were correlated with one another and with gray matter intensity in the right pars opercularis region of the inferior frontal gyrus in healthy subjects. Gray matter intensity of this region was also correlated with methamphetamine craving. Compared to healthy subjects, methamphetamine-dependent subjects exhibited lower gray matter intensity in this region, worse motor inhibitory control, and less success in affect regulation. These findings suggest that self-control in different psychological domains involves a common substrate in the right pars opercularis, and that successful self-control depends on integrity of this substrate.
inhibitory control; emotion; addiction; methamphetamine; inferior frontal gyrus (IFG); ventrolateral prefrontal cortex
Over the past year, a heated discussion about ‘circular' or ‘nonindependent' analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in ≤100 words per question. Although divergent views remain on some of the questions, there is also a substantial convergence of opinion, which we have summarized in a consensus box. The box provides the best current answers that the five authors could agree upon.
brain imaging; functional magnetic resonance imaging; imaging; neuroimaging; statistical methods
The ability to flexibly respond to changes in the environment is critical for adaptive behavior. Reversal learning (RL) procedures test adaptive response updating when contingencies are altered. We used functional magnetic resonance imaging to examine brain areas that support specific RL components. We compared neural responses to RL and initial learning (acquisition) to isolate reversal-related brain activation independent of cognitive control processes invoked during initial feedback-based learning. Lateral orbitofrontal cortex (OFC) was more activated during reversal than acquisition, suggesting its relevance for reformation of established stimulus–response associations. In addition, the dorsal anterior cingulate (dACC) and right inferior frontal gyrus (rIFG) correlated with change in postreversal accuracy. Because optimal RL likely requires suppression of a prior learned response, we hypothesized that similar regions serve both response inhibition (RI) and inhibition of learned associations during reversal. However, reversal-specific responding and stopping (requiring RI and assessed via the stop-signal task) revealed distinct frontal regions. Although RI-related regions do not appear to support inhibition of prepotent learned associations, a subset of these regions, dACC and rIFG, guide actions consistent with current reward contingencies. These regions and lateral OFC represent distinct neural components that support behavioral flexibility important for adaptive learning.
cognitive control; fMRI; orbitofrontal cortex; response inhibition; reversal learning