Studies of adults with attention-deficit/hyperactivity disorder (ADHD) have suggested that they have deficient response inhibition, but findings concerning the neural correlates of inhibition in this patient population are inconsistent. We used the Stop-Signal task and functional magnetic resonance imaging (fMRI) to compare neural activation associated with response inhibition between adults with ADHD (N = 35) and healthy comparison subjects (N = 62), and in follow-up tests to examine the effect of current medication use and symptom severity. There were no differences in Stop-Signal task performance or neural activation between ADHD and control participants. Among the ADHD participants, however, significant differences were associated with current medication, with individuals taking psychostimulants (N = 25) showing less stopping-related activation than those not currently receiving psychostimulant medication (N = 10). Follow-up analyses suggested that this difference in activation was independent of symptom severity. These results provide evidence that deficits in inhibition-related neural activation persist in a subset of adult ADHD individuals, namely those individuals currently taking psychostimulants. These findings help to explain some of the disparities in the literature, and advance our understanding of why deficits in response inhibition are more variable in adult, as compared with child and adolescent, ADHD patients.
Inhibitory control; Hyperactivity; Psychostimulants; Functional magnetic resonance imaging (fMRI); Adults; Stop-Signal task
The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.
The Stop-signal task (SST), in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision-making, a drift diffusion model of simple decisions was fitted to SST data from Go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the Go stimulus correlated with greater activation in the right frontal pole for both Go and Stop trials. On Stop trials stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and basal ganglia. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control, and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.
drift-diffusion model; fMRI; Individual differences; inhibitory control; Stop signal task
Adolescent women with a parental history of depression are at high risk for the onset of major depressive disorder (MDD). Cognitive theories suggest this vulnerability involves deficits in cognitive control over emotional information. Among adolescent women with and without a parental history of depression, we examined differences in connectivity using resting state functional connectivity analysis within a network associated with cognitive control over emotional information.
Twenty-four depression-naïve adolescent women underwent resting state functional magnetic resonance imaging (fMRI). They were assigned to high-risk (n = 11) and low-risk (n = 13) groups based their parents’ depression history. Seed based functional connectivity analysis was used to examine group differences in connectivity within a network associated with cognitive control.
High-risk adolescents had lower levels of connectivity between a right inferior prefrontal region and other critical nodes of the attention control network, including right middle frontal gyrus and right supramarginal gyrus. Further, greater severity of the parents’ worst episode of depression was associated with altered cognitive control network connectivity in their adolescent daughters.
Depressed parents may transmit depression vulnerability to their adolescent daughters via alterations in functional connectivity within neural circuits that underlie cognitive control of emotional information.
depression vulnerability; adolescence; parental history of depression; cognitive control network; resting-state fMRI; functional connectivity
It is believed that choice behavior reveals the underlying value of goods. The subjective values of stimuli can be changed through reward-based learning mechanisms as well as by modifying the description of the decision problem, but it has yet to be shown that preferences can be manipulated by perturbing intrinsic values of individual items. Here we show that the value of food items can be modulated by the concurrent presentation of an irrelevant auditory cue to which subjects must make a simple motor response (i.e. cue-approach training). Follow-up tests show that the effects of this pairing on choice lasted at least two months after prolonged training. Eye-tracking during choice confirmed that cue-approach training increased attention to the cued items. Neuroimaging revealed the neural signature of a value change in the form of amplified preference-related activity in ventromedial prefrontal cortex.
Despite a national reduction in the prevalence of cigarette smoking, ~19% of the adult U.S. population persists in this behavior, with the highest prevalence among 18–25-year-olds. Given that the choice to smoke imposes a known health risk, clarification of brain function related to decision-making, particularly involving risk-taking, in smokers may inform prevention and smoking cessation strategies.
This study aimed to compare brain function related to decision-making in young smokers and nonsmokers.
The Balloon Analogue Risk Task (BART) is a computerized risky decision-making task in which participants pump virtual balloons, each pump associated with an incremental increase in potential payoff on a given trial but also with greater risk of balloon explosion and loss of payoff. We used this task to compare brain activation associated with risky decision-making in smokers (n=18) and nonsmokers (n=25) while they performed the BART during functional magnetic resonance imaging (fMRI). The participants were young men and women, 17–21 years of age.
Risk level (number of pumps) modulated brain activation in the right dorsolateral and ventrolateral prefrontal cortices more in smokers than in nonsmokers; and smoking severity (Heaviness of Smoking Index) was positively related to this modulation in an adjacent frontal region.
Given evidence for involvement of the right dorsolateral and ventrolateral prefrontal cortices in inhibitory control, these findings suggest that young smokers have a different contribution of prefrontal cortical substrates to risky decision-making than nonsmokers. Future studies are warranted to determine whether the observed neurobiological differences precede or result from smoking.
nicotine; functional MRI; prefrontal cortex; decision-making
One central goal in cognitive neuroscience of learning and memory is to characterize the neural processes that lead to long-lasting episodic memory. In addition to the stronger frontoparietal activity, greater category- or item-specific cortical representation during encoding, as measured by pattern similarity (PS), is also associated with better subsequent episodic memory. Nevertheless, it is unknown whether frontoparietal activity and cortical PS reflect distinct mechanisms. To address this issue, we reanalyzed previous data (Xue G, Dong Q, Chen C, Lu ZL, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science. 330:97, Experiment 3) using a novel approach based on combined activation-based and information-based analyses. The results showed that across items, stronger frontoparietal activity was associated with greater PS in distributed brain regions, including those where the PS was predictive of better subsequent memory. Nevertheless, the item-specific PS was still associated with later episodic memory after controlling the effect of frontoparietal activity. Our results suggest that one possible mechanism of frontoparietal activity on episodic memory encoding is via enhancing PS, resulting in more unique and consistent input to the medial temporal lobe. In addition, they suggest that PS might index additional processes, such as pattern reinstatement as a result of study-phase retrieval, that contribute to episodic memory encoding.
episodic memory; functional MRI; goal-directed process; representation similarity; subsequent memory effect
To overcome unhealthy behaviors, one must be able to make better choices. Changing food preferences is an important strategy in addressing the obesity epidemic and its accompanying public health risks. However, little is known about how food preferences can be effectively affected and what neural systems support such changes. In this study we investigated a novel extensive training paradigm where participants chose from specific pairs of palatable junk food items and were rewarded for choosing the items with lower subjective value over higher value ones. In a later probe phase, when choices were made for real consumption, participants chose the lower-valued item more often in the trained pairs compared to untrained pairs. We replicated the behavioral results in an independent sample of participants while they were scanned with fMRI. We found that as training progressed there was decreased recruitment of regions that have been previously associated with cognitive control, specifically left dorsolateral prefrontal cortex (dlPFC) and bilateral parietal cortices. Furthermore, we found that connectivity of the left dlPFC was greater with primary motor regions by the end of training for choices of lower-valued items that required exertion of self-control, suggesting a formation of a stronger stimulus-response association. These findings demonstrate that it is possible to influence food choices through training, and that this training is associated with a decreasing need for top-down frontoparietal control. The results suggest that training paradigms may be promising as the basis for interventions to influence real world food preferences.
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
In the past, power analyses were not that common for fMRI studies, but recent advances in power calculation techniques and software development are making power analyses much more accessible. As a result, power analyses are more commonly expected in grant applications proposing fMRI studies. Even though the software is somewhat automated, there are important decisions to be made when setting up and carrying out a power analysis. This guide provides tips on carrying out power analyses, including obtaining pilot data, defining a region of interest and other choices to help create reliable power calculations.
functional magnetic resonance imaging; classification analysis; MVPA; beta series estimation; rapid event-related design
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
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
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
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
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
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
Genetic studies are rapidly identifying variants that shape risk for disorders of human cognition, but the question of how such variants predispose to neuropsychiatric disease remains. Noninvasive human brain imaging allows assessment of the brain in vivo, and the combination of genetics and imaging phenotypes remains one of the only ways to explore functional genotype-phenotype associations in human brain. Common variants in contactin-associated protein-like 2 (CNTNAP2), a neurexin superfamily member, have been associated with several allied neurodevelopmental disorders, including autism and specific language impairment, and CNTNAP2 is highly expressed in frontal lobe circuits in the developing human brain. Using functional neuroimaging, we have demonstrated a relationship between frontal lobar connectivity and common genetic variants in CNTNAP2. These data provide a mechanistic link between specific genetic risk for neurodevelopmental disorders and empirical data implicating dysfunction of long-range connections within the frontal lobe in autism. The convergence between genetic findings and cognitive-behavioral models of autism provides evidence that genetic variation at CNTNAP2 predisposes to diseases such asautism in part through modulation of frontal lobe connectivity.
While much is known about the neural regions recruited in the human brain when a dominant motor response becomes inappropriate and must be stopped, less is known about the regions that support switching to a new, appropriate, response. Using fMRI with two variants of the stop-signal paradigm that require either stopping altogether or switching to a different response, we examined the brain systems involved in these two forms of executive control. Both stopping trials and switching trials showed common recruitment of the right inferior frontal gyrus, pre-supplementary motor area, and midbrain. Contrasting switching trials with stopping trials showed activation similar to that observed on response trials (where the initial response remains appropriate and no control is invoked), whereas there were no regions that showed significantly greater activity for stopping trials compared to switching trials. These results show that response switching can be supported by the same neural systems as response inhibition, and suggest that the same mechanism of rapid, nonselective response inhibition that is thought to support speeded response stopping can also support speeded response switching when paired with execution of the new, appropriate, response.
Repeated study improves memory, but the underlying neural mechanisms of this improvement are not well understood. Using functional magnetic resonance imaging and representational similarity analysis of brain activity, we found that, compared with forgotten items, subsequently remembered faces and words showed greater similarity in neural activation across multiple study in many brain regions, including (but not limited to) the regions whose mean activities were correlated with subsequent memory. This result addresses a longstanding debate in the study of memory by showing that successful episodic memory encoding occurs when the same neural representations are more precisely reactivated across study episodes, rather than when patterns of activation are more variable across time.
While many advanced mixed-effects models have been proposed and are used in fMRI, the simplest, ordinary least squares (OLS), is still the one that is most widely used. A survey of 90 papers found that 92% of group fMRI analyses used OLS. Despite the widespread use, this simple approach has never been thoroughly justified and evaluated; for example, the typical reference for the method is a conference abstract, (Holmes and Friston, 1998), which has been referenced over 400 times.
In this work we fully derive the simplified method in a general setting and carefully identify the homogeneity assumptions it is based on. We examine the specificity (Type I error rate) of the OLS method under heterogeneity in the one-sample case and find that the OLS method is valid, with only slight conservativeness. Surprisingly, a Satterthwaite approximation for effective degrees of freedom only makes the method more conservative, instead of more accurate. While other authors have highlighted the inferior power of the OLS method relative to optimal mixed effects methods under heterogeneity, we revisit these results and find the power differences very modest.
While statistical methods that make the best use of the data are always to be preferred, software or other practical concerns may require the use of the simple OLS group modeling. In such cases, we find that group mean inferences will be valid under the null hypothesis and will have nearly optimal sensitivity under the alternative.
Functional Magnetic Resonance Imaging; ordinary least squares; general linear model; specificity; hypothesis testing; Two-Stage Summary Statistics; Study Design
We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence.
functional magnetic resonance imaging; region of interest analysis; bias; statistics; multiple comparisons
While methamphetamine addiction has been associated with both impulsivity and striatal dopamine D2/D3 receptor deficits, human studies have not directly linked the latter two entities. We therefore compared methamphetamine-dependent and healthy control subjects using the Barratt Impulsiveness Scale (version 11, BIS-11) and positron emission tomography with [18F]fallypride to measure striatal dopamine D2/D3 receptor availability. The methamphetamine-dependent subjects reported recent use of the drug 3.3 g per week, and a history of using methamphetamine, on average, for 12.5 years. They had higher scores than healthy control subjects on all BIS-11 impulsiveness subscales (p < 0.001). Volume-of-interest analysis found lower striatal D2/D3 receptor availability in methamphetamine-dependent than in healthy control subjects (p < 0.01) and a negative relationship between impulsiveness and striatal D2/D3 receptor availability in the caudate nucleus and nucleus accumbens that reached statistical significance in methamphetamine-dependent subjects. Combining data from both groups, voxelwise analysis indicated that impulsiveness was related to D2/D3 receptor availability in left caudate nucleus and right lateral putamen/claustrum (p < 0.05, determined by threshold-free cluster enhancement). In separate group analyses, correlations involving the head and body of the caudate and the putamen of methamphetamine-dependent subjects, and the lateral putamen/claustrum of control subjects were observed at a weaker threshold (p < 0.12 corrected). The findings suggest that low striatal D2/D3 receptor availability may mediate impulsive temperament and thereby influence addiction.
methamphetamine; impulsivity; addiction; dopamine; receptor; striatum
Arterial spin labeling (ASL) data are typically differenced, sometimes after interpolation, as part of preprocessing before statistical analysis in fMRI. While this process can reduce the number of time points by half, it simplifies the subsequent signal and noise models (i.e., smoothed box-car predictors and white noise). In this paper, we argue that ASL data are best viewed in the same data analytic framework as BOLD fMRI data, in that all scans are modeled and colored noise is accommodated. The data are not differenced, but the control/label effect is implicitly built into the model. While the models using differenced data may seem easier to implement, we show that differencing models fit with ordinary least squares either produce biased estimates of the standard errors or suffer from a loss in efficiency. The main disadvantage to our approach is that non-white noise must be modeled in order to yield accurate standard errors, however, this is a standard problem that has been solved for BOLD data, and the very same software can be used to account for such autocorrelated noise.
Arterial spin labeling; Perfusion; Blood flow; Functional MRI (fMRI); Statistical analysis; Statistical power; Functional imaging; Signal processing