TY - JOUR A1 - Hilger, Kirsten A1 - Häge, Anne-Sophie A1 - Zedler, Christina A1 - Jost, Michael A1 - Pauli, Paul T1 - Virtual reality to understand pain-associated approach behaviour: a proof-of-concept study JF - Scientific Reports N2 - Pain-associated approach and avoidance behaviours are critically involved in the development and maintenance of chronic pain. Empirical research suggests a key role of operant learning mechanisms, and first experimental paradigms were developed for their investigation within a controlled laboratory setting. We introduce a new Virtual Reality paradigm to the study of pain-related behaviour and investigate pain experiences on multiple dimensions. The paradigm evaluates the effects of three-tiered heat-pain stimuli applied contingent versus non-contingent with three types of arm movements in naturalistic virtual sceneries. Behaviour, self-reported pain-related fear, pain expectancy and electrodermal activity were assessed in 42 healthy participants during an acquisition phase (contingent movement-pain association) and a modification phase (no contingent movement-pain association). Pain-associated approach behaviour, as measured by arm movements followed by a severe heat stimulus, quickly decreased in-line with the arm movement-pain contingency. Slower effects were observed in fear of movement-related pain and pain expectancy ratings. During the subsequent modification phase, the removal of the pain contingencies modified all three indices. In both phases, skin conductance responses resemble the pattern observed for approach behaviour, while skin conductance levels equal the pattern observed for the self-ratings. Our findings highlight a fast reduction in approach behaviour in the face of acute pain and inform about accompanying psychological and physiological processes. We discuss strength and limitations of our paradigm for future investigations with the ultimate goal of gaining a comprehensive understanding of the mechanisms involved in chronic pain development, maintenance, and its therapy. KW - anxiety KW - human behaviour Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357817 VL - 13 ER - TY - JOUR A1 - Thiele, Jonas A. A1 - Richter, Aylin A1 - Hilger, Kirsten T1 - Multimodal brain signal complexity predicts human intelligence JF - eNeuro N2 - Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits. KW - brain signal complexity KW - cognitive ability KW - EEG KW - intelligence KW - microstates KW - resting-state Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312949 VL - 10 IS - 2 ER - TY - JOUR A1 - Wehrheim, Maren H. A1 - Faskowitz, Joshua A1 - Sporns, Olaf A1 - Fiebach, Christian J. A1 - Kaschube, Matthias A1 - Hilger, Kirsten T1 - Few temporally distributed brain connectivity states predict human cognitive abilities JF - NeuroImage N2 - 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. KW - functional connectivity KW - resting state KW - machine learning KW - predictive modeling KW - general cognitive ability Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349874 VL - 277 ER - TY - JOUR A1 - Kiser, Dominik P. A1 - Gromer, Daniel A1 - Pauli, Paul A1 - Hilger, Kirsten T1 - A virtual reality social conditioned place preference paradigm for humans: Does trait social anxiety affect approach and avoidance of virtual agents? JF - Frontiers in Virtual Reality N2 - Approach and avoidance of positive and negative social cues are fundamental to prevent isolation and ensure survival. High trait social anxiety is characterized by an avoidance of social situations and extensive avoidance is a risk factor for the development of social anxiety disorder (SAD). Therefore, experimental methods to assess social avoidance behavior in humans are essential. The social conditioned place preference (SCPP) paradigm is a well-established experimental paradigm in animal research that is used to objectively investigate social approach–avoidance mechanisms. We retranslated this paradigm for human research using virtual reality. To this end, 58 healthy adults were exposed to either a happy- or angry-looking virtual agent in a specific room, and the effects of this encounter on dwell time as well as evaluation of this room in a later test without an agent were examined. We did not observe a general SCPP effect on dwell time or ratings but discovered a moderation by trait social anxiety, in which participants with higher trait social anxiety spent less time in the room in which the angry agent was present before, suggesting that higher levels of trait social anxiety foster conditioned social avoidance. However, further studies are needed to verify this observation and substantiate an association with social anxiety disorder. We discussed the strengths, limitations, and technical implications of our paradigm for future investigations to more comprehensively understand the mechanisms involved in social anxiety and facilitate the development of new personalized treatment approaches by using virtual reality. KW - retranslational research KW - conditioned place preference KW - approach–avoidance KW - social anxiety KW - virtual reality KW - personality traits KW - individual differences Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-293564 SN - 2673-4192 VL - 3 ER -