Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

Value-based choices are influenced both by risk in potential outcomes and by whether outcomes reflect potential gains or losses. These variables are held to be related in a specific fashion, manifest in risk aversion for gains and risk seeking for losses. Instead, we hypothesized that there are independent impacts of risk and loss on choice such that, depending on context, subjects can show either risk aversion for gains and risk seeking for losses or the exact opposite. We demonstrate this independence in a gambling task, by selectively reversing a loss-induced effect (causing more gambling for gains than losses and the reverse) while leaving risk aversion unaffected. Consistent with these dissociable behavioral impacts of risk and loss, fMRI data revealed dissociable neural correlates of these variables, with parietal cortex tracking risk and orbitofrontal cortex and striatum tracking loss. Based on our neural data, we hypothesized that risk and loss influence action selection through approach-avoidance mechanisms, a hypothesis supported in an experiment in which we show valence and risk-dependent reaction time effects in line with this putative mechanism. We suggest that in the choice process risk and loss can independently engage approach-avoidance mechanisms. This can provide a novel explanation for how risk influences action selection and explains both classically described choice behavior as well as behavioral patterns not predicted by existing theory.

While there is a significant relationship between the CYP450 genotype and serum concentrations of escitalopram and nortriptyline, the genotypes are not predictive of differences in treatment response for either drug. Furthermore, differences in antidepressant serum concentrations are not associated with variability in treatment response.

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.

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