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Adolescence has been linked to an enhanced tolerance of uncertainty and risky behavior and is possibly connected to an increased response toward rewards. However, previous research has produced inconsistent findings. To investigate whether these findings are due to different reward probabilities used in the experimental design, we extended a monetary incentive delay (MID) task by including three different reward probabilities. Using functional magnetic resonance imaging, 25 healthy adolescents and 22 adults were studied during anticipation of rewards in the VS. Differently colored cue stimuli indicated either a monetary or verbal trial and symbolized different reward probabilities, to which the participants were blinded. Results demonstrated faster reaction times for lower reward probabilities (33%) in both age groups. Adolescents were slower through all conditions and had less activation on a neural level. Imaging results showed a three-way interaction between age group x condition x reward probability with differences in percent signal change between adolescents and adults for the high reward probabilities (66%, 88%) while adolescents demonstrated differences for the lowest (33%). Therefore, previous inconsistent findings could be due to different reward probabilities, which makes examining these crucial for a better understanding of adolescent and adult behavior.
Talking about emotion and sharing emotional experiences is a key component of human interaction. Specifically, individuals often consider the reactions of other people when evaluating the meaning and impact of an emotional stimulus. It has not yet been investigated, however, how emotional arousal ratings and physiological responses elicited by affective stimuli are influenced by the rating of an interaction partner. In the present study, pairs of participants were asked to rate and communicate the degree of their emotional arousal while viewing affective pictures. Strikingly, participants adjusted their arousal ratings to match up with their interaction partner. In anticipation of the affective picture, the interaction partner’s arousal ratings correlated positively with activity in anterior insula and prefrontal cortex. During picture presentation, social influence was reflected in the ventral striatum, that is, activity in the ventral striatum correlated negatively with the interaction partner’s ratings. Results of the study show that emotional alignment through the influence of another person’s communicated experience has to be considered as a complex phenomenon integrating different components including emotion anticipation and conformity.
Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants’ choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors.
Rationale: Both attention deficit-/hyperactivity disorder (ADHD) and alcohol use disorder (AUD) are accompanied by deficits in response inhibition. Furthermore, the prevalence of comorbidity of ADHD and AUD is high. However, there is a lack of research on whether the same neuronal subprocesses of inhibition (i.e., interference inhibition, action withholding and action cancellation) exhibit deficits in both psychiatric disorders. Methods: We examined these three neural subprocesses of response inhibition in patient groups and healthy controls: non-medicated individuals with ADHD (ADHD; N = 16), recently detoxified and abstinent individuals with alcohol use disorder (AUD; N = 15), and healthy controls (HC; N = 15). A hybrid response inhibition task covering interference inhibition, action withholding, and action cancellation was applied using a 3T functional magnetic resonance imaging (fMRI). Results: Individuals with ADHD showed an overall stronger hypoactivation in attention related brain areas compared to AUD or HC during action withholding. Further, this hypoactivation was more accentuated during action cancellation. Individuals with AUD recruited a broader network, including the striatum, compared to HC during action withholding. During action cancellation, however, they showed hypoactivation in motor regions. Additionally, specific neural activation profiles regarding group and subprocess became apparent. Conclusions: Even though deficits in response inhibition are related to both ADHD and AUD, neural activation and recruited networks during response inhibition differ regarding both neuronal subprocesses and examined groups. While a replication of this study is needed in a larger sample, the results suggest that tasks have to be carefully selected when examining neural activation patterns of response inhibition either in research on various psychiatric disorders or transdiagnostic questions.
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.
Human deep sleep is characterized by reduced sensory activity, responsiveness to stimuli, and conscious awareness. Given its ubiquity and reversible nature, it represents an attractive paradigm to study the neural changes which accompany the loss of consciousness in humans. In particular, the deepest stages of sleep can serve as an empirical test for the predictions of theoretical models relating the phenomenology of consciousness with underlying neural activity. A relatively recent shift of attention from the analysis of evoked responses toward spontaneous (or “resting state”) activity has taken place in the neuroimaging community, together with the development of tools suitable to study distributed functional interactions. In this review we focus on recent functional Magnetic Resonance Imaging (fMRI) studies of spontaneous activity during sleep and their relationship with theoretical models for human consciousness generation, considering the global workspace theory, the information integration theory, and the dynamical core hypothesis. We discuss the venues of research opened by these results, emphasizing the need to extend the analytic methodology in order to obtain a dynamical picture of how functional interactions change over time and how their evolution is modulated during different conscious states. Finally, we discuss the need to experimentally establish absent or reduced conscious content, even when studying the deepest sleep stages.
Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.
In recent years, a number of functional and structural neuroimaging studies have investigated the neural bases of aggressive and violent behaviour in children and adolescents. Most functional neuroimaging studies have persued the hypothesis that pathological aggression is a consequence of deficits in the neural circuits involved in emotion processing. There is converging evidence for abnormal neural responses to emotional stimuli in youths with a propensity towards aggressive behaviour. In addition, recent neuroimaging work has suggested that aggressive behaviour is also associated with abnormalities in neural processes that subserve both the inhibitory control of behaviour and the flexible adaptation of behaviour in accord with reinforcement information. Structural neuroimaging studies in children and adolescents with conduct problems are still scarce, but point to deficits in brain structures involved in the processing of social information and in the regulation of social and goal-directed behaviour. The indisputable progress that this research field has made in recent years notwithstanding, the overall picture is still rather patchy and there are inconsistencies between studies that await clarification. Despite this, we attempt to provide an integrated view on the neural abnormalities that may contribute to various forms of juvenile aggression and violence, and discuss research strategies that may help to provide a more profound understanding of these important issues in the future. Keywords: aggression, violence, conduct disorder, fMRI, brain imaging, psychiatry
We examined the neural signatures of stimulus features in visual working memory (WM) by integrating functional magnetic resonance imaging (fMRI) and event-related potential data recorded during mental manipulation of colors, rotation angles, and color–angle conjunctions. The N200, negative slow wave, and P3b were modulated by the information content of WM, and an fMRI-constrained source model revealed a progression in neural activity from posterior visual areas to higher order areas in the ventral and dorsal processing streams. Color processing was associated with activity in inferior frontal gyrus during encoding and retrieval, whereas angle processing involved right parietal regions during the delay interval. WM for color–angle conjunctions did not involve any additional neural processes. The finding that different patterns of brain activity underlie WM for color and spatial information is consistent with ideas that the ventral/dorsal “what/where” segregation of perceptual processing influences WM organization. The absence of characteristic signatures of conjunction-related brain activity, which was generally intermediate between the 2 single conditions, suggests that conjunction judgments are based on the coordinated activity of these 2 streams. Keywords: EEG, fMRI, source analysis, visual, working memory
The neural processing of speech and music is still a matter of debate. A long tradition that assumes shared processing capacities for the two domains contrasts with views that assume domain-specific processing. We here contribute to this topic by investigating, in a functional magnetic imaging (fMRI) study, ecologically valid stimuli that are identical in wording and differ only in that one group is typically spoken (or silently read), whereas the other is sung: poems and their respective musical settings. We focus on the melodic properties of spoken poems and their sung musical counterparts by looking at proportions of significant autocorrelations (PSA) based on pitch values extracted from their recordings. Following earlier studies, we assumed a bias of poem-processing towards the left and a bias for song-processing on the right hemisphere. Furthermore, PSA values of poems and songs were expected to explain variance in left- vs. right-temporal brain areas, while continuous liking ratings obtained in the scanner should modulate activity in the reward network. Overall, poem processing compared to song processing relied on left temporal regions, including the superior temporal gyrus, whereas song processing compared to poem processing recruited more right temporal areas, including Heschl's gyrus and the superior temporal gyrus. PSA values co-varied with activation in bilateral temporal regions for poems, and in right-dominant fronto-temporal regions for songs. Continuous liking ratings were correlated with activity in the default mode network for both poems and songs. The pattern of results suggests that the neural processing of poems and their musical settings is based on their melodic properties, supported by bilateral temporal auditory areas and an additional right fronto-temporal network known to be implicated in the processing of melodies in songs. These findings take a middle ground in providing evidence for specific processing circuits for speech and music in the left and right hemisphere, but simultaneously for shared processing of melodic aspects of both poems and their musical settings in the right temporal cortex. Thus, we demonstrate the neurobiological plausibility of assuming the importance of melodic properties in spoken and sung aesthetic language alike, along with the involvement of the default mode network in the aesthetic appreciation of these properties.