Are electrophysiological correlates of response inhibition linked to impulsivity and compulsivity? A machine-learning analysis of a Go/Nogo task.

Heightened impulsivity and compulsivity are often found in association with both dysfunctional everyday behavior and with psychopathology. Impulsivity and compulsivity are also linked to alterations in behavioral response inhibition and its electrophysiological correlates. However, they are rarely examined jointly and their effect outside of clinical samples is still disputed.

Aberrant neural representation of food stimuli in women with acute anorexia nervosa predicts treatment outcome and is improved in weight restored individuals.

Anorexia nervosa (AN) has been associated with altered reward processing. We recently reported greater neural response in secondary visual areas when processing visual food stimuli in acutely underweight AN patients (acAN). In order to examine whether the observed alterations are indicative of acute undernutrition or a potential trait marker of AN, we set out to assess neural responses in acAN and in individuals weight-recovered from AN (recAN).

Brain state kinematics and the trajectory of task performance improvement.

Dimensionality reduction techniques offer a unique perspective on brain state dynamics, in which systems-level activity can be tracked through the engagement of a small number of component trajectories. Used in combination with neuroimaging data collected during the performance of cognitive tasks, these approaches can expose the otherwise latent dimensions upon which the brain reconfigures in order to facilitate cognitive performance. Here, we utilized Principal Component Analysis to transform parcellated BOLD timeseries from an fMRI dataset in which 70 human subjects performed an instruction based visuomotor learning task into orthogonal low-dimensional components.

Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis.

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).

Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia.

Rapid instructed task learning (RITL) is the uniquely human ability to transform task information into goal-directed behavior without relying on trial-and-error learning. RITL is a core cognitive process supported by functional brain networks. In patients with schizophrenia, RITL ability is impaired, but the role of functional network connectivity in these RITL deficits is unknown.

Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting.

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.

A comparison of fMRI and behavioral models for predicting inter-temporal choices.

In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices.

Predicting Real-Life Self-Control From Brain Activity Encoding the Value of Anticipated Future Outcomes.

Deficient self-control leads to shortsighted decisions and incurs severe personal and societal costs. Although neuroimaging has advanced our understanding of neural mechanisms underlying self-control, the ecological validity of laboratory tasks used to assess self-control remains largely unknown. To increase ecological validity and to test a specific hypothesis about the mechanisms underlying real-life self-control, we combined functional MRI during value-based decision-making with smartphone-based assessment of real-life self-control in a large community sample ( = 194).

Neural representation of newly instructed rule identities during early implementation trials.

By following explicit instructions, humans instantaneously get the hang of tasks they have never performed before. We used a specially calibrated multivariate analysis technique to uncover the elusive representational states during the first few implementations of arbitrary rules such as 'for coffee, press red button' following their first-time instruction. Distributed activity patterns within the ventrolateral prefrontal cortex (VLPFC) indicated the presence of neural representations specific of individual stimulus-response (S-R) rule identities, preferentially for conditions requiring the memorization of instructed S-R rules for correct performance.

Unbiased Analysis of Item-Specific Multi-Voxel Activation Patterns Across Learning.

Recent work has highlighted that multi-voxel pattern analysis (MVPA) can be severely biased when BOLD response estimation involves systematic imbalance in model regressor correlations. This problem occurs in situations where trial types of interest are temporally dependent and the associated BOLD activity overlaps. For example, in learning paradigms early and late learning stage trials are inherently ordered.

Monitor yourself! Deficient error-related brain activity predicts real-life self-control failures.

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.

Large-scale coupling dynamics of instructed reversal learning.

The ability to rapidly learn from others by instruction is an important characteristic of human cognition. A recent study found that the rapid transfer from initial instructions to fluid behavior is supported by changes of functional connectivity between and within several large-scale brain networks, and particularly by the coupling of the dorsal attention network (DAN) with the cingulo-opercular network (CON). In the present study, we extended this approach to investigate how these brain networks interact when stimulus-response mappings are altered by novel instructions.

Integration and segregation of large-scale brain networks during short-term task automatization.

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions.

Investigating the Impact of a Genome-Wide Supported Bipolar Risk Variant of MAD1L1 on the Human Reward System.

Recent genome-wide association studies have identified MAD1L1 (mitotic arrest deficient-like 1) as a susceptibility gene for bipolar disorder and schizophrenia. The minor allele of the single-nucleotide polymorphism (SNP) rs11764590 in MAD1L1 was associated with bipolar disorder. Both diseases, bipolar disorder and schizophrenia, are linked to functional alterations in the reward system.

The neural basis of integrating pre- and post-response information for goal-directed actions.

A fundamental prerequisite for goal-directed action is to encode the contingencies between responses (R) producing specific outcomes (O) in specific stimulus conditions (S). The present study aimed to characterize the functional neuroanatomy of different associational sub-components of such S-R-O contingencies during the first few trials of exposure. We devised a novel paradigm that was suited to distinguish BOLD activation patterns related to S-R, R-O, and the full S-R-O contingency.

CREB1 Genotype Modulates Adaptive Reward-Based Decisions in Humans.

Cyclic AMP response element-binding protein (CREB) contributes to adaptation of mesocorticolimbic networks by modulating activity-regulated transcription and plasticity in neurons. Activity or expression changes of CREB in the nucleus accumbens (NAc) and orbital frontal cortex (OFC) interact with behavioral changes during reward-motivated learning. However, these findings from animal models have not been evaluated in humans.

Sparse regularization techniques provide novel insights into outcome integration processes.

By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns. Furthermore, the most recent refinement, the Graph Net, explicitly takes the 3D-structure of fMRI data into account.

CACNA1C genotype explains interindividual differences in amygdala volume among patients with schizophrenia.

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.