Affective traits link to reliable neural markers of incentive anticipation.

While theorists have speculated that different affective traits are linked to reliable brain activity during anticipation of gains and losses, few have directly tested this prediction. We examined these associations in a community sample of healthy human adults (n=52) as they played a Monetary Incentive Delay task while undergoing functional magnetic resonance imaging (FMRI). Factor analysis of personality measures revealed that subjects independently varied in trait Positive Arousal and trait Negative Arousal. In a subsample (n=14) retested over 2.5years later, left nucleus accumbens (NAcc) activity during anticipation of large gains (+$5.00) and right anterior insula activity during anticipation of large losses (-$5.00) showed significant test-retest reliability (intraclass correlations>0.50, p's<0.01). In the full sample (n=52), trait Positive Arousal correlated with individual differences in left NAcc activity during anticipation of large gains, while trait Negative Arousal correlated with individual differences in right anterior insula activity during anticipation of large losses. Associations of affective traits with neural activity were not attributable to the influence of other potential confounds (including sex, age, wealth, and motion). Together, these results demonstrate selective links between distinct affective traits and reliably-elicited activity in neural circuits associated with anticipation of gain versus loss. The findings thus reveal neural markers for affective dimensions of healthy personality, and potentially for related psychiatric symptoms.

Interpretable whole-brain prediction analysis with GraphNet.

Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional "mass univariate" techniques that fit data one voxel at a time. In the functional magnetic resonance imaging (fMRI) literature, this has led to broad application of "off-the-shelf" classification and regression methods. These generic approaches allow investigators to use ready-made algorithms to accurately decode perceptual, cognitive, or behavioral states from distributed patterns of neural activity. However, when applied to correlated whole-brain fMRI data these methods suffer from coefficient instability, are sensitive to outliers, and yield dense solutions that are hard to interpret without arbitrary thresholding. Here, we develop variants of the Graph-constrained Elastic-Net (GraphNet), a fast, whole-brain regression and classification method developed for spatially and temporally correlated data that automatically yields interpretable coefficient maps (Grosenick et al., 2009b). GraphNet methods yield sparse but structured solutions by combining structured graph constraints (based on knowledge about coefficient smoothness or connectivity) with a global sparsity-inducing prior that automatically selects important variables. Because GraphNet methods can efficiently fit regression or classification models to whole-brain, multiple time-point data sets and enhance classification accuracy relative to volume-of-interest (VOI) approaches, they eliminate the need for inherently biased VOI analyses and allow whole-brain fitting without the multiple comparison problems that plague mass univariate and roaming VOI ("searchlight") methods. As fMRI data are unlikely to be normally distributed, we (1) extend GraphNet to include robust loss functions that confer insensitivity to outliers, (2) equip them with "adaptive" penalties that asymptotically guarantee correct variable selection, and (3) develop a novel sparse structured Support Vector GraphNet classifier (SVGN). When applied to previously published data (Knutson et al., 2007), these efficient whole-brain methods significantly improved classification accuracy over previously reported VOI-based analyses on the same data (Grosenick et al., 2008; Knutson et al., 2007) while discovering task-related regions not documented in the original VOI approach. Critically, GraphNet estimates fit to the Knutson et al. (2007) data generalize well to out-of-sample data collected more than three years later on the same task but with different subjects and stimuli (Karmarkar et al., submitted for publication). By enabling robust and efficient selection of important voxels from whole-brain data taken over multiple time points (>100,000 "features"), these methods enable data-driven selection of brain areas that accurately predict single-trial behavior within and across individuals.

Does higher education hone cognitive functioning and learning efficacy? Findings from a large and diverse sample.

Attending school is a multifaceted experience. Students are not only exposed to new knowledge but are also immersed in a structured environment in which they need to respond flexibly in accordance with changing task goals, keep relevant information in mind, and constantly tackle novel problems. To quantify the cumulative effect of this experience, we examined retrospectively and prospectively, the relationships between educational attainment and both cognitive performance and learning. We analyzed data from 196,388 subscribers to an online cognitive training program. These subscribers, ages 15-60, had completed eight behavioral assessments of executive functioning and reasoning at least once. Controlling for multiple demographic and engagement variables, we found that higher levels of education predicted better performance across the full age range, and modulated performance in some cognitive domains more than others (e.g., reasoning vs. processing speed). Differences were moderate for Bachelor's degree vs. High School (d = 0.51), and large between Ph.D. vs. Some High School (d = 0.80). Further, the ages of peak cognitive performance for each educational category closely followed the typical range of ages at graduation. This result is consistent with a cumulative effect of recent educational experiences, as well as a decrement in performance as completion of schooling becomes more distant. To begin to characterize the directionality of the relationship between educational attainment and cognitive performance, we conducted a prospective longitudinal analysis. For a subset of 69,202 subscribers who had completed 100 days of cognitive training, we tested whether the degree of novel learning was associated with their level of education. Higher educational attainment predicted bigger gains, but the differences were small (d = 0.04-0.37). Altogether, these results point to the long-lasting trace of an effect of prior cognitive challenges but suggest that new learning opportunities can reduce performance gaps related to one's educational history.

Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior.

Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.

Enhancing Cognitive Abilities with Comprehensive Training: A Large, Online, Randomized, Active-Controlled Trial.

BACKGROUND: A variety of studies have demonstrated gains in cognitive ability following cognitive training interventions. However, other studies have not shown such gains, and questions remain regarding the efficacy of specific cognitive training interventions. Cognitive training research often involves programs made up of just one or a few exercises, targeting limited and specific cognitive endpoints. In addition, cognitive training studies typically involve small samples that may be insufficient for reliable measurement of change. Other studies have utilized training periods that were too short to generate reliable gains in cognitive performance. METHODS: The present study evaluated an online cognitive training program comprised of 49 exercises targeting a variety of cognitive capacities. The cognitive training program was compared to an active control condition in which participants completed crossword puzzles. All participants were recruited, trained, and tested online (N = 4,715 fully evaluable participants). Participants in both groups were instructed to complete one approximately 15-minute session at least 5 days per week for 10 weeks. RESULTS: Participants randomly assigned to the treatment group improved significantly more on the primary outcome measure, an aggregate measure of neuropsychological performance, than did the active control group (Cohen's d effect size = 0.255; 95% confidence interval = [0.198, 0.312]). Treatment participants showed greater improvements than controls on speed of processing, short-term memory, working memory, problem solving, and fluid reasoning assessments. Participants in the treatment group also showed greater improvements on self-reported measures of cognitive functioning, particularly on those items related to concentration compared to the control group (Cohen's d = 0.249; 95% confidence interval = [0.191, 0.306]). CONCLUSION: Taken together, these results indicate that a varied training program composed of a number of tasks targeted to different cognitive functions can show transfer to a wide range of untrained measures of cognitive performance. TRIAL REGISTRATION: ClinicalTrials.gov NCT-02367898.

Inferring affect from fMRI data.

Neuroimaging findings are often interpreted in terms of affective experience, but researchers disagree about the advisability or even possibility of such inferences, and few frameworks explicitly link these levels of analysis. Here, we suggest that the spatial and temporal resolution of functional magnetic resonance imaging (fMRI) data could support inferences about affective states. Specifically, we propose that fMRI nucleus accumbens (NAcc) activity is associated with positive arousal, whereas a combination of anterior insula activity and NAcc activity is associated with negative arousal. This framework implies quantifiable and testable inferences about affect from fMRI data, which may ultimately inform predictions about approach and avoidance behavior. We consider potential limits on neurally inferred affect before highlighting theoretical and practical benefits.