150 Psychologie
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Efficient processing of visual environment necessitates the integration of incoming sensory evidence with concurrent contextual inputs and mnemonic content from our past experiences. To delineate how this integration takes place in the brain, we studied modulations of feedback neural patterns in non-stimulated areas of the early visual cortex in humans (i.e., V1 and V2). Using functional magnetic resonance imaging and multivariate pattern analysis, we show that both, concurrent contextual and time-distant mnemonic information, coexist in V1/V2 as feedback signals. The extent to which mnemonic information is reinstated in V1/V2 depends on whether the information is retrieved episodically or semantically. These results demonstrate that our stream of visual experience contains not just information from the visual surrounding, but also memory-based predictions internally generated in the brain.
Systemic therapy considers the complex dynamics of relational factors and resources contributing to psychological symptoms. Negative maintaining factors have been well researched for people suffering from Alcohol-use Disorders (AUD). However, we know little about the complex dynamics of these negative factors and resources. We interviewed fifty-five participants suffering or fully remitted from Alcohol-use disorders in this cross-sectional study (M = 52 years; 33% female). The interviews focused on relational factors (e.g., social support and social negativity) referring to a Support Social Network and a Craving Social Network (CSN). The CSN included all significant others who were associated with craving situations. We compared the network characteristics of the group suffering from Alcohol-use Disorders (n = 38) to a fully remitted control group (n = 17). The abstinent group with full remission named on average fewer individuals in the CSNs. They had lower social negativity mean scores in the Support Social Network compared to the non-remitted group (d = 0.74). In the CSN, the mean scores of social support were significantly higher than the median for both groups (d = 2.50). These findings reveal the complex interplay of relational patterns contributing to the etiology, maintenance, and recovery from Alcohol-use disorders. A successful recovery can be linked to increased social resources and reduced relations associated with craving. However, craving-associated relations represent an important source of social support. Future research should investigate this ambivalence for the systemic perspective on the explanation and treatment of Alcohol-use disorders.
Cultural and biographical influences on the expression of emotions manifest themselves in so-called “display rules.” These rules determine the time, intensity, and situations in which an emotion is expressed. To date, only a small number of empirical studies deal with this transformation of how migrants, who are faced with a new culture, may change their emotional expression. The present, cross-sectional study focuses on changes in anger expression as part of a complex acculturation process among Iranian migrants. To this end, Iranian citizens in Iran (n = 61), German citizens (n = 61), and Iranian migrants in Germany (n = 60) were compared in terms of anger expression behavior and acculturation strategy (assimilation, separation, integration, marginalization) was assessed among the migrants, using the Frankfurt Acculturation Scale (FRACC). A questionnaire developed in a preliminary study was used to measure anger expression via subjective anger experience and anger expression within 16 hypothetical situations. Multivariate Analyses of Variance (MANOVA) revealed that Iranians and Iranian migrants reported higher anger experience ratings than Germans and directed their anger more often inward (anger-in). Further findings suggest that transformation processes may have affected Iranian migrants in terms of suppressed anger (anger-in): Iranian migrants with a higher orientation toward German culture reported lower average anger-in scores. These results suggest that there was different emotional expression among Iranian migrants, depending on their acculturation. The results provide new insight into socio-cultural and individual adjustment processes.
Trajectories of internalizing disorders and behavioral addictions are still largely unknown. Research shows that both disorders are highly comorbid. Previous longitudinal studies have focused on associations between internalizing disorders and behavioral addictions using screening instruments. Our aim was to develop and examine a theory-based model of trajectories, according to which internalizing disorders foster symptoms of Internet use disorders, mediated by a reward deprivation and maladaptive emotion regulation. We applied clinically relevant measures for depression and social anxiety in a prospective longitudinal study with a 12-month follow-up investigation. On the basis of an at-risk population of 476 students (mean age = 14.99 years, SD = 1.99), we investigated the predictive influence of clinically relevant depression and social anxiety at baseline (t1) on Internet use disorder symptoms at 12-month follow-up (t2) using multiple linear regression analyses. Our results showed that both clinically relevant depression and social anxiety significantly predicted symptom severity of Internet use disorders one year later after controlling for baseline symptoms of Internet use disorders, gender and age. These results remained robust after including both depression and social anxiety simultaneously in the model, indicating an independent influence of both predictors on Internet use disorder symptoms. The present study enhances knowledge going beyond a mere association between internalizing disorders and Internet use disorders. To our knowledge, this is the first study investigating clinically relevant depression and social anxiety to predict future Internet use disorder symptoms at 12-month follow-up. In line with our model of trajectories, a significant temporal relationship between clinically relevant internalizing disorders and Internet use disorder symptoms at 12-month follow-up was confirmed. Further studies should investigate the mediating role of reward deprivation and maladaptive emotion regulation, as postulated in our model. One implication of these findings is that clinicians should pay particular attention to the increased risk of developing behavioral addictions for adolescents with depression and social anxiety.
Interest is an important factor for successful learning that has been the subject of intensive research for decades. Although interest in nature is of great importance for environmental education, to date there is no valid and reliable measurement tool. Therefore, the purpose of this study was to develop and test a scale for interest in nature, the Nature Interest Scale (NIS). In study 1, nine items were selected based on the three dimensions of the psychological interest construct to represent interest in nature. The factor structure of this new measurement instrument, was tested using confirmatory factor analyses. The results show that the instrument represents the three dimensions of the interest construct well. In study 2 the validity (discriminant and convergent validity) as well as the reliability (internal consistency, composite reliability, test-retest reliability) of the NIS were demonstrated. In study 3, the applicability of the NIS was tested with a different target group, students with learning disabilities. The results of this factor analysis also confirm the factor structure of the scale. Thus, this study provides a valid and reliable measurement tool for individual interest in nature that can be used for future research.
Highlights
• Brain connectivity states identified by cofluctuation strength.
• CMEP as new method to robustly predict human traits from brain imaging data.
• Network-identifying connectivity ‘events’ are not predictive of cognitive ability.
• Sixteen temporally independent fMRI time frames allow for significant prediction.
• Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths.
Abstract
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips of visual events across ten human subjects. We use the videos’ extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.
Aim: There is ongoing debate about the role of cortical and subcortical brain areas in force modulation. In a whole-brain approach, we sought to investigate the anatomical basis of grip force whilst acknowledging interindividual differences in connectivity patterns. We tested if brain lesion mapping in patients with unilateral motor deficits can inform whole-brain structural connectivity analysis in healthy controls to uncover the networks underlying grip force.
Methods: Using magnetic resonance imaging (MRI) and whole-brain voxel-based morphometry in chronic stroke patients (n=55) and healthy controls (n=67), we identified the brain regions in both grey and white matter significantly associated with grip force strength. The resulting statistical parametric maps (SPMs) provided seed areas for whole-brain structural covariance analysis in a large-scale community dwelling cohort (n=977) that included beyond volume estimates, parameter maps sensitive to myelin, iron and tissue water content.
Results: The SPMs showed symmetrical bilateral clusters of correlation between upper limb motor performance, basal ganglia, posterior insula and cortico-spinal tract. The covariance analysis with the seed areas derived from the SPMs demonstrated a widespread anatomical pattern of brain volume and tissue properties, including both cortical, subcortical nodes of motor networks and sensorimotor areas projections.
Conclusion: We interpret our covariance findings as a biological signature of brain networks implicated in grip force. The data-driven definition of seed areas obtained from chronic stroke patients showed overlapping structural covariance patterns within cortico-subcortical motor networks across different tissue property estimates. This cumulative evidence lends face validity of our findings and their biological plausibility.
Despite the recent popularity of predictive processing models of brain function, the term prediction is often instantiated very differently across studies. These differences in definition can substantially change the type of cognitive or neural operation hypothesised and thus have critical implications for the corresponding behavioural and neural correlates during visual perception. Here, we propose a five-dimensional scheme to characterise different parameters of prediction. Namely, flow of information, mnemonic origin, specificity, complexity, and temporal precision. We describe these dimensions and provide examples of their application to previous work. Such a characterisation not only facilitates the integration of findings across studies, but also helps stimulate new research questions.