Tobias U. Hauser

The feedback-related negativity (FRN) revisited: New insights into the localization, meaning and network organization

Changes in response contingencies require adjusting ones assumptions about outcomes of behaviors. Such adaptation processes are driven by reward prediction error (RPE) signals which reflect the inadequacy of expectations. Signals resembling RPEs are known to be encoded by mesencephalic dopamine neurons projecting to the striatum and frontal regions. Although regions that process RPEs, such as the dorsal anterior cingulate cortex (dACC), have been identified, only indirect evidence links timing and network organization of RPE processing in humans. In electroencephalography (EEG), which is well known for its high temporal resolution, the feedback-related negativity (FRN) has been suggested to reflect RPE processing. Recent studies, however, suggested that the FRN might reflect surprise, which would correspond to the absolute, rather than the signed RPE signals. Furthermore, the localization of the FRN remains a matter of debate. In this simultaneous EEG-functional magnetic resonance imaging (fMRI) study, we localized the FRN directly using the superior spatial resolution of fMRI without relying on any spatial constraint or other assumption. Using two different single-trial approaches, we consistently found a cluster within the dACC. One analysis revealed additional activations of the salience network. Furthermore, we evaluated the effect of signed RPEs and surprise signals on the FRN amplitude. We considered that both signals are usually correlated and found that only surprise signals modulate the FRN amplitude. Last, we explored the pathway of RPE signals using dynamic causal modeling (DCM). We found that the surprise signals are directly projected to the source region of the FRN. This finding contradicts earlier theories about the network organization of the FRN, but is in line with a recent theory stating that dopamine neurons also encode surprise-like saliency signals. Our findings crucially advance the understanding of the FRN. We found compelling evidence that the FRN originates from the dACC. Furthermore, we clarified the functional role of the FRN, and determined the role of the dACC within the RPE network. These findings should enable us to study the processing of surprise and adjustment signals in the dACC in healthy and also in psychiatric patients.

Role of the Medial Prefrontal Cortex in Impaired Decision Making in Juvenile Attention-Deficit/Hyperactivity Disorder

IMPORTANCE Attention-deficit/hyperactivity disorder (ADHD) has been associated with deficient decision making and learning. Models of ADHD have suggested that these deficits could be caused by impaired reward prediction errors (RPEs). Reward prediction errors are signals that indicate violations of expectations and are known to be encoded by the dopaminergic system. However, the precise learning and decision-making deficits and their neurobiological correlates in ADHD are not well known. OBJECTIVE To determine the impaired decision-making and learning mechanisms in juvenile ADHD using advanced computational models, as well as the related neural RPE processes using multimodal neuroimaging. DESIGN, SETTING, AND PARTICIPANTS Twenty adolescents with ADHD and 20 healthy adolescents serving as controls (aged 12-16 years) were examined using a probabilistic reversal learning task while simultaneous functional magnetic resonance imaging and electroencephalogram were recorded. MAIN OUTCOMES AND MEASURES Learning and decision making were investigated by contrasting a hierarchical Bayesian model with an advanced reinforcement learning model and by comparing the model parameters. The neural correlates of RPEs were studied in functional magnetic resonance imaging and electroencephalogram. RESULTS Adolescents with ADHD showed more simplistic learning as reflected by the reinforcement learning model (exceedance probability, Px = .92) and had increased exploratory behavior compared with healthy controls (mean [SD] decision steepness parameter β: ADHD, 4.83 [2.97]; controls, 6.04 [2.53]; P = .02). The functional magnetic resonance imaging analysis revealed impaired RPE processing in the medial prefrontal cortex during cue as well as during outcome presentation (P < .05, family-wise error correction). The outcome-related impairment in the medial prefrontal cortex could be attributed to deficient processing at 200 to 400 milliseconds after feedback presentation as reflected by reduced feedback-related negativity (ADHD, 0.61 [3.90] μV; controls, -1.68 [2.52] μV; P = .04). CONCLUSIONS AND RELEVANCE The combination of computational modeling of behavior and multimodal neuroimaging revealed that impaired decision making and learning mechanisms in adolescents with ADHD are driven by impaired RPE processing in the medial prefrontal cortex. This novel, combined approach furthers the understanding of the pathomechanisms in ADHD and may advance treatment strategies.

Enhancing performance in numerical magnitude processing and mental arithmetic using transcranial Direct Current Stimulation (tDCS)

The ability to accurately process numerical magnitudes and solve mental arithmetic is of highest importance for schooling and professional career. Although impairments in these domains in disorders such as developmental dyscalculia (DD) are highly detrimental, remediation is still sparse. In recent years, transcranial brain stimulation methods such as transcranial Direct Current Stimulation (tDCS) have been suggested as a treatment for various neurologic and neuropsychiatric disorders. The posterior parietal cortex (PPC) is known to be crucially involved in numerical magnitude processing and mental arithmetic. In this study, we evaluated whether tDCS has a beneficial effect on numerical magnitude processing and mental arithmetic. Due to the unclear lateralization, we stimulated the left, right as well as both hemispheres simultaneously in two experiments. We found that left anodal tDCS significantly enhanced performance in a number comparison and a subtraction task, while bilateral and right anodal tDCS did not induce any improvements compared to sham. Our findings demonstrate that the left PPC is causally involved in numerical magnitude processing and mental arithmetic. Furthermore, we show that these cognitive functions can be enhanced by means of tDCS. These findings encourage to further investigate the beneficial effect of tDCS in the domain of mathematics in healthy and impaired humans.

Conflict monitoring and error processing: new insights from simultaneous EEG-fMRI

Error processing and conflict monitoring are essential executive functions for goal directed actions and adaptation to conflicting information. Although medial frontal regions such as the anterior cingulate cortex (ACC) and the pre-supplementary motor area (pre-SMA) are known to be involved in these functions, there is still considerable heterogeneity regarding their spatio-temporal activations. The timing of these functions has been associated with two separable event-related potentials (ERPs) usually localized to the medial frontal wall, one during error processing (ERN--error related negativity) and one during conflict monitoring (N2). In this study we aimed to spatially and temporally dissociate conflict and error processing using simultaneously recorded EEG and fMRI data from a modified Flanker task in healthy adults. We demonstrate a spatial dissociation of conflict monitoring and error processing along the medial frontal wall, with selective conflict level dependent activation of the SMA/pre-SMA. Activation to error processing was located in the ACC, rostral cingulate zone (RCZ) and pre-SMA. The EEG-informed fMRI analysis revealed that stronger ERN amplitudes are associated with increased activation in a large coherent cluster comprising the ACC, RCZ and pre-SMA, while N2 amplitudes increased with activation in the pre-SMA. Conjunction analysis of EEG-informed fMRI revealed common activation of ERN and N2 in the pre-SMA and divergent activation in the RCZ. No conjoint activation between error processing and conflict monitoring was found with standard fMRI analysis along the medial frontal wall. Our fMRI findings clearly demonstrate that conflict monitoring and error processing are spatially dissociable along the medial frontal wall. Moreover, the overlap of ERN- and N2-informed fMRI activation in the pre-SMA provides new evidence that these ERP components share conflict related processing functions and are thus not completely separable.

Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development

Adolescence is associated with quickly changing environmental demands which require excellent adaptive skills and high cognitive flexibility. Feedback-guided adaptive learning and cognitive flexibility are driven by reward prediction error (RPE) signals, which indicate the accuracy of expectations and can be estimated using computational models. Despite the importance of cognitive flexibility during adolescence, only little is known about how RPE processing in cognitive flexibility deviates between adolescence and adulthood. In this study, we investigated the developmental aspects of cognitive flexibility by means of computational models and functional magnetic resonance imaging (fMRI). We compared the neural and behavioral correlates of cognitive flexibility in healthy adolescents (12–16 years) to adults performing a probabilistic reversal learning task. Using a modified risk-sensitive reinforcement learning model, we found that adolescents learned faster from negative RPEs than adults. The fMRI analysis revealed that within the RPE network, the adolescents had a significantly altered RPE-response in the anterior insula. This effect seemed to be mainly driven by increased responses to negative prediction errors. In summary, our findings indicate that decision making in adolescence goes beyond merely increased reward-seeking behavior and provides a developmental perspective to the behavioral and neural mechanisms underlying cognitive flexibility in the context of reinforcement learning.

Unexpected arousal modulates the influence of sensory noise on confidence

Human perception is invariably accompanied by a graded feeling of confidence that guides metacognitive awareness and decision-making. It is often assumed that this arises solely from the feed-forward encoding of the strength or precision of sensory inputs. In contrast, interoceptive inference models suggest that confidence reflects a weighted integration of sensory precision and expectations about internal states, such as arousal. Here we test this hypothesis using a novel psychophysical paradigm, in which unseen disgust-cues induced unexpected, unconscious arousal just before participants discriminated motion signals of variable precision. Across measures of perceptual bias, uncertainty, and physiological arousal we found that arousing disgust cues modulated the encoding of sensory noise. Furthermore, the degree to which trial-by-trial pupil fluctuations encoded this nonlinear interaction correlated with trial level confidence. Our results suggest that unexpected arousal regulates perceptual precision, such that subjective confidence reflects the integration of both external sensory and internal, embodied states. DOI: http://dx.doi.org/10.7554/eLife.18103.001

Computational psychiatry of ADHD: Neural gain impairments across marrian levels of analysis

Attention-deficit hyperactivity disorder (ADHD), one of the most common psychiatric disorders, is characterised by unstable response patterns across multiple cognitive domains. However, the neural mechanisms that explain these characteristic features remain unclear. Using a computational multilevel approach, we propose that ADHD is caused by impaired gain modulation in systems that generate this phenotypic increased behavioural variability. Using Marrs three levels of analysis as a heuristic framework, we focus on this variable behaviour, detail how it can be explained algorithmically, and how it might be implemented at a neural level through catecholamine influences on corticostriatal loops. This computational, multilevel, approach to ADHD provides a framework for bridging gaps between descriptions of neuronal activity and behaviour, and provides testable predictions about impaired mechanisms.

The PhysIO toolbox for modeling physiological noise in fMRI data

BACKGROUND Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. NEW METHODS We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention. RESULTS We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). COMPARISON WITH EXISTING METHODS The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. CONCLUSIONS Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

Imaging genetics in obsessive-compulsive disorder: Linking genetic variations to alterations in neuroimaging

Obsessive-compulsive disorder (OCD) occurs in ∼1-3% of the general population, and its often rather early onset causes major disabilities in the everyday lives of patients. Although the heritability of OCD is between 35 and 65%, many linkage, association, and genome-wide association studies have failed to identify single genes that exhibit high effect sizes. Several neuroimaging studies have revealed structural and functional alterations mainly in cortico-striato-thalamic loops. However, there is also marked heterogeneity across studies. These inconsistencies in genetic and neuroimaging studies may be due to the heterogeneous and complex phenotypes of OCD. Under the consideration that genetic variants may also influence neuroimaging in OCD, researchers have started to combine both domains in the field of imaging genetics. Here, we conducted a systematic search of PubMed and Google Scholar literature for articles that address genetic imaging in OCD and related disorders (published through March 2014). We selected 8 publications that describe the combination of imaging genetics with OCD, and extended it with 43 publications of comorbid psychiatric disorders. The most promising findings of this systematic review point to the involvement of variants in genes involved in the serotonergic (5-HTTLPR, HTR2A), dopaminergic (COMT, DAT), and glutamatergic (SLC1A1, SAPAP) systems. However, the field of imaging genetics must be further explored, best through investigations that combine multimodal imaging techniques with genetic profiling, particularly profiling techniques that employ polygenetic approaches, with much larger sample sizes than have been used up to now.

Striatal structure and function predict individual biases in learning to avoid pain.

Significance Our ability to learn how to avoid harm is critical for maintaining physical and mental health. However, excessive harm avoidance can be maladaptive, as evident in psychiatric disorders such as avoidant personality disorder and obsessive-compulsive disorder. We therefore investigated the neural factors underlying individual imbalances in harm avoidance behavior. Our findings show that such imbalances can be predicted by the function and structure of an individual’s striatum, a brain region that is critical for goal-directed decisionmaking. Moreover, the neural signals expressed in this region revealed key processes through which individuals learn to avoid harm. These findings highlight a neural basis for imbalanced harm avoidance behavior, extreme forms of which may contribute to psychiatric pathology. Pain is an elemental inducer of avoidance. Here, we demonstrate that people differ in how they learn to avoid pain, with some individuals refraining from actions that resulted in painful outcomes, whereas others favor actions that helped prevent pain. These individual biases were best explained by differences in learning from outcome prediction errors and were associated with distinct forms of striatal responses to painful outcomes. Specifically, striatal responses to pain were modulated in a manner consistent with an aversive prediction error in individuals who learned predominantly from pain, whereas in individuals who learned predominantly from success in preventing pain, modulation was consistent with an appetitive prediction error. In contrast, striatal responses to success in preventing pain were consistent with an appetitive prediction error in both groups. Furthermore, variation in striatal structure, encompassing the region where pain prediction errors were expressed, predicted participants’ predominant mode of learning, suggesting the observed learning biases may reflect stable individual traits. These results reveal functional and structural neural components underlying individual differences in avoidance learning, which may be important contributors to psychiatric disorders involving pathological harm avoidance behavior.