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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.
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.
The visual system encompasses about 20% of the cerebral cortex1 and plays a pivotal role in higher-order cognitive processes such as attention and working memory. Cognitive impairments constitute a central role in neuropsychiatric disorders such as schizophrenia (SZ). Impairments are described in visual perceptual processes including contrast, and emotion discrimination as well as in the ability to identify visual irregularities and in higher-order cognition like visual attention and working memory. Furthermore, perceptual and higher-order cognitive processes are part of the Research Domain Criteria (RDoC) project that aims to develop dimensional and transdiagnostic constructs with defined links to specific brain circuits.Therefore, the detailed study of the visual system using functional magnetic resonance imaging (fMRI) is essential to understand the processes in healthy individuals but also in populations with neuropsychiatric disorders. Visual mapping techniques include functional localizer tasks to map functionally defined regions like the fusiform face area (FFA), retinotopic mapping to map specific brain regions that are retinotopically organized in full, and visual-field localizer paradigms to define circumscribed areas within retinotopically organized areas.Thus, the latter allow studying local information processing in early visual areas. Despite advances in neuroimaging techniques, analyses of fMRI data at the group-level are impeded by interindividual macroanatomical variability. This reduces the reliability to accurately define visual areas particularly at the group-level and decreases statistical power. Single-subject based solutions for this problem are not appropriate. Analyses after volume-based alignment (VBA) and primary surface-based analyses without macroanatomical alignment do not increase macroanatomical correspondence sufficiently. Cortex-based alignment (CBA) approaches are recommended as an alternative technique to address this obstacle. However, CBA has not been evaluated for visual-field localizer paradigms. Therefore, we aimed to evaluate potential benefits of CBA for an attention-enhanced visual field localizer paradigm that maps circumscribed regions in retinotopically organized visual areas. Since previous studies solely compared surface-based data before and after CBA, we aimed to compare all three techniques: (1) a volume-based alignment (VBA), (2) a surface-based data set without (SBAV) and (3) a surface- based data set with macroanatomical alignment (CBA). Furthermore, we sought to define regions of interest (ROI) that subsequently can be used for the study of higher-order cognitive processes. Also, we aimed to investigate whether CBA facilitates the study of functional asymmetries in early visual areas as these were described in previous studies. Healthy volunteers (n=50) underwent fMRI in a 3- Tesla Siemens Trio scanner while performing an attention-enhanced visual field localizer paradigm. Our task consisted of a series of flickering, black-and white colored checkerboard stimuli that randomly appeared at one of four locations comprising the participants’ visual quadrants. In 25% of the trials the centrally located squares briefly changed their color to yellow (target trial). Participants had to indicate detection of a target by button press. Data analysis was conducted using Brain Voyager 20.6. Our approach for macroanatomical alignment included a high-resolution, multiscale curvature driven alignment procedure minimizing interindividual macroanatomical variability. Here, each folding pattern was aligned to a dynamically updated group average. Thus, we counteracted a possible confounding effect of a suboptimal selection of an individual target brain with a folding pattern deviating considerably from the cohort average. Group ROIs after CBA showed increased spatial consistency, vertical symmetry, and an increase of size. This was corroborated by an increase in the probability of activation overlap of up to 86%. CBA increased macroanatomical correspondence and thus ameliorated results of multi-subject ROI analyses. Functional differences in the form of a downward bias in visual hemifields were measured with increased reliability. In summary, our findings provide clear evidence for the superiority of CBA for the study of local information processing in early visual cortex at the group-level. This approach is of relevance for the study of visual dysfunction in neuropsychiatric disorders including schizophrenia as they show impaired visual processing that in turn impacts higher-order cognitive processes and in consequence functional outcome. In addition, our attention-enhanced visual field localizer paradigm will be useful for machine learning approaches such as multivariate pattern analysis decoding local information processes and connectivity patterns.
Bipolar disorder (BD) and major depressive disorder (MDD) are severe mood disorders that belong to the most debilitating diseases worldwide. Differentiating both mood disorders often poses a major clinical challenge, leading to frequent misdiagnoses. Objective biomarkers able to differentiate individuals with BD and MDD therefore represent a psychiatric research field of utmost importance. Recent studies have applied resting-state fMRI paradigms and found promising results differentiating both disorders based on the acquired data. However, most of these studies have focused their efforts on acutely depressed patients. Thus, it remains unclear whether the aberrations remain in a symptomless disease state.
The here presented study addresses these issues by evaluating the ability to differentiate both disorders from one another by conducting a between-group comparison of functional brain network connectivity (FNC) obtained from resting-state fMRI data. Data were collected from 20 BD, 15 MDD patients and 30 age- and gender-matched healthy controls (HC). Graph theoretical analyses were applied to detect differences in functional network organization between the groups on a global and regional network level.
Network analysis detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality.
These results indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.
Emotional instability, difficulties in social adjustment, and disinhibited behavior are the most common symptoms of the psychiatric comorbidities in juvenile myoclonic epilepsy (JME). This psychopathology has been associated with dysfunctions of mesial-frontal brain circuits. The present work is a first direct test of this link and adapted a paradigm for probing frontal circuits during empathy for pain. Neural and psychophysiological parameters of pain empathy were assessed by combining functional magnetic resonance imaging (fMRI) with simultaneous pupillometry in 15 JME patients and 15 matched healthy controls. In JME patients, we observed reduced neural activation of the anterior cingulate cortex (ACC), the anterior insula (AI), and the ventrolateral prefrontal cortex (VLPFC). This modulation was paralleled by reduced pupil dilation during empathy for pain in patients. At the same time, pupil dilation was positively related to neural activity of the ACC, AI, and VLPFC. In JME patients, the ACC additionally showed reduced functional connectivity with the primary and secondary somatosensory cortex, areas fundamentally implicated in processing the somatic cause of another's pain. Our results provide first evidence that alterations of mesial-frontal circuits directly affect psychosocial functioning in JME patients and draw a link of pupil dynamics with brain activity during emotional processing. The findings of reduced pain empathy related activation of the ACC and AI and aberrant functional integration of the ACC with somatosensory cortex areas provide further evidence for this network's role in social behavior and helps explaining the JME psychopathology and patients' difficulties in social adjustment.
Understanding the underlying mechanisms that link psychopathology and physical comorbidities in schizophrenia is crucial since decreased physical fitness and overweight pose major risk factors for cardio-vascular diseases and decrease the patients’ life expectancies. We hypothesize that altered reward anticipation plays an important role in this. We implemented the Monetary Incentive Delay task in a MR scanner and a fitness test battery to compare schizophrenia patients (SZ, n = 43) with sex- and age-matched healthy controls (HC, n = 36) as to reward processing and their physical fitness. We found differences in reward anticipation between SZs and HCs, whereby increased activity in HCs positively correlated with overall physical condition and negatively correlated with psychopathology. On the other handy, SZs revealed stronger activity in the posterior cingulate cortex and in cerebellar regions during reward anticipation, which could be linked to decreased overall physical fitness. These findings demonstrate that a dysregulated reward system is not only responsible for the symptomatology of schizophrenia, but might also be involved in physical comorbidities which could pave the way for future lifestyle therapy interventions.
Early maternal care may counteract familial liability for psychopathology in the reward circuitry
(2018)
Reward processing is altered in various psychopathologies and has been shown to be susceptible to genetic and environmental influences. Here, we examined whether maternal care may buffer familial risk for psychiatric disorders in terms of reward processing. Functional magnetic resonance imaging during a monetary incentive delay task was acquired in participants of an epidemiological cohort study followed since birth (N = 172, 25 years). Early maternal stimulation was assessed during a standardized nursing/playing setting at the age of 3 months. Parental psychiatric disorders (familial risk) during childhood and the participants’ previous psychopathology were assessed by diagnostic interview. With high familial risk, higher maternal stimulation was related to increasing activation in the caudate head, the supplementary motor area, the cingulum and the middle frontal gyrus during reward anticipation, with the opposite pattern found in individuals with no familial risk. In contrast, higher maternal stimulation was associated with decreasing caudate head activity during reward delivery and reduced levels of attention deficit hyperactivity disorder (ADHD) in the high-risk group. Decreased caudate head activity during reward anticipation and increased activity during delivery were linked to ADHD. These findings provide evidence of a long-term association of early maternal stimulation on both adult neurobiological systems of reward underlying externalizing behavior and ADHD during development.