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Despite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one's own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. Brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life self-control. [ABSTRACT FROM AUTHOR]

In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting procedure, which considerably increases computation time compared to estimating unregularized or L2-regularized models, and complicates the application of L1-regularization on whole-brain data and large sample sizes. Here we provide several functions for estimating L1-regularized models that are optimized for the mass-univariate analysis approach. The package includes a parallel implementation of the coordinate descent algorithm for CPU-only systems and two implementations of the alternating direction method of multipliers algorithm requiring a GPU device. While the core algorithms are implemented in C++/CUDA, data input/output and parameter settings can be conveniently handled via Matlab. The CPU-based implementation is highly memory-efficient and provides considerable speed-up compared to the standard implementation not optimized for the mass-univariate approach. Further acceleration can be achieved on systems equipped with a CUDA-enabled GPU. Using the fastest GPU-based implementation, computation time for whole-brain estimates can be reduced from 9 h to 5 min in an exemplary data setting. Overall, the provided package facilitates the use of L1-regularization for fMRI activation analyses and enables an efficient employment of L1-regularization on whole-brain data and large sample sizes. [ABSTRACT FROM AUTHOR]

Despite their immense relevance, the neurocognitive mechanisms underlying real-life self-control failures (SCFs) are insufficiently understood. Whereas previous studies have shown that SCFs were associated with decreased activity in the right inferior frontal gyrus (rIFG; a region involved in cognitive control), here we consider the possibility that the reduced implementation of cognitive control in individuals with low self-control may be due to impaired performance monitoring. Following a brain-as-predictor approach, we combined experience sampling of daily SCFs with functional magnetic resonance imaging (fMRI) in a Stroop task. In our sample of 118 participants, proneness to SCF was reliably predicted by low error-related activation of a performance-monitoring network (comprising anterior mid-cingulate cortex, presupplementary motor area, and anterior insula), low posterror rIFG activation, and reduced posterror slowing. Remarkably, these neural and behavioral measures predicted variability in SCFs beyond what was predicted by self-reported trait self-control. These results suggest that real-life SCFs may result from deficient performance monitoring, leading to reduced recruitment of cognitive control after responses that conflict with superordinate goals. [ABSTRACT FROM AUTHOR]

Affective deficits are one common denominator of schizophrenia (SZ), bipolar disorder (BD) and obsessive compulsive disorder (OCD) with the amygdala indicated as one of the major structures involved in emotion regulation. Previous findings of differences in amygdala volume between healthy controls and patients with SZ, BD or OCD diverge with respect to the affected hemisphere, size and direction of the effect. Variability in the CACNA1C gene has been linked to BD, SZ as well as structural and functional variation in the amygdala in healthy people and patients with BD. We were interested to investigate whether amygdala volumes differ between hemispheres, diagnostic or genotype groups, and whether any interactive effects exist. We combined genotyping of SNP rs1006737 in CACNA1C with structural MRI measurements of relative gray matter (GM) amygdala volume in patients with SZ, BD or OCD as well as healthy controls ( N = 72). The CACNA1C genotype showed a significant effect on relative GM amygdala volume in patients with SZ. There was a significant left versus right relative GM amygdala volume decrease in patients with SZ or BD. The effects of hemisphere and diagnosis (controls vs. patients with SZ) on relative GM amygdala volume were genotype specific. Our data suggest that the CACNA1C genotype may account for some heterogeneity in the effects of hemisphere and diagnosis on amygdala volume when comparing patients with SZ and controls and point to disturbed Ca-signaling as a plausible mechanism contributing to the pathology in patients with SZ. [ABSTRACT FROM AUTHOR]