Deutsch Intern
Department of Psychology I – Clinical Psychology and Psychotherapy

Networks of Behavior and Cognition

Network Neuroscience of Intelligence and Personality Differences

Intelligence describes our ability to reason, to understand complex ideas and to learn from experiences. It is associated with important life outcomes like educational or occupational success and seem to play a role even for health and longevity. Although it is one of the oldest psychological constructs, it is still of high relevance and constitutes a reliable indicator of general cognitive ability. Understanding the biological bases of human intelligence is an important scientific aim and former neuroscientific research has identified differences in brain structure and brain function covarying with individual variations in intelligence.

Network Neuroscience is a scientific discipline transferring methods from physics and mathematics to the investigation of human neuroimaging data (MRI, fMRI). Recently, it has been shown to be especially fruitful in the context of individual differences.

Our lab focusses on Personality Network Neuroscience as a new field of investigation applying graph-theoretical network approaches to established psychological theories about intelligence and human personality. By using its rich methodology and by adopting a system-level perspective on the brain, we aim to advance biologically-plausible theories of intelligence and personality, e.g., by unraveling the complex interaction between general intelligence and controlled attention.

Finally, our interdisciplinary team uses methods from Machine Learning to further develop connectome-based predictive modelling approaches. By using this methodology, we aim to go beyond correlative associations and to achieve robust out-of-sample predictions, i.e., predicting individual intelligence test scores on the basis of dynamic brain connectivity. Most of our research endeavors are based on MRI and fMRI data from large data bases such as the Human Connectome Project (www.humanconnectomeproject.org) and in general, our team fosters principle of Open and Reproducible Science.

Fear of Pain, Approach and Avoidance – in the Context of Stress and Individual Variation

Chronic pain represents a severe and common burden with enormous effects on patients everyday life. In accordance to the Fear Avoidance Model of chronic pain (Vlaeyen & Linton, 2012) mechanisms of fear learning and avoidance behavior play a major role in the development and the maintenance of chronic pain conditions. The model proposes a self-reinforcing vicious circle of fear, avoidance, disability and pain. However, only a small proportion of people enters such a vicious circle after an acute pain episode (e.g., after an injury or an medical intervention) and the factors that determine whether a person may enter this circle or not (and develops chronic pain) are still an open question.

Our team focuses explicitly on this question and investigates the influence of stress and stable individual differences (e.g., personality factors) on the acquisition of Fear of Pain. Therefore, we transfer methods from traditional fear conditioning research to Virtual Reality. A new experimental paradigm is developed allowing to experimentally induce (and extinguish) Fear of Pain as well as to investigate effects of context and motor imaginary. Finally, we use various biophysiological assessments (e.g., electrodermal activity, EDA, cortisol concentration, heat rate) and electroencephalographical (EEG) recordings to clarify the biological underpinnings of state Fear of Pain, trait Fear of Pain, and to understand the mechanisms of potential modulators (e.g., stress, personality).

Currently, our research endeavors focus on five complementary questions:

  • Can Fear of Pain be learned (and extinguished) in the Virtual Reality?
  • Do persons with higher disposition for pain-related fear (trait FoP) show stronger physiological reactions to acute stress?
  • Does acute stress lead to an increased learning rate of pain-related fear (state FoP)?
  • What are the underlying neurophysiological mechanisms of state FoP acquisition and do intrinsic neural oscillation patterns relate to stable individual differences in Fear of Pain (trait FoP)?
  • Can we use positive motor imaginary to reduce Fear of Pain (also in chronic pain patients)?

DeYoung, C. G.*, Hilger, K.*, Hanson, J. L., Abend, R., Allen, T., Beaty, R., … Wacker, J. (In press). Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences, Journal of Cognitive Neuroscience. (Preprint: PsyArXiv, 2024-7. https://doi.org/10.31219/osf.io/bjn62)

Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (under review, Nature Communications Biology). Structural-Functional Brain Network Coupling During Task Performance Reveals Intelligence-Relevant Communication Strategies. (Preprint: bioRxiv, https://doi.org/10.1101/2024.10.29.620941).

Hilger, K., Talic, I., & Renner, K-H. (under Review). Individual Differences in the Correspondence Between Psychological and Physiological Stress Indicators.
bioRxiv, 2024.08.23.609328. https://doi.org/10.1101/2024.08.23.609328

DeYoung, C. G.*, Hilger, K.*, Hanson, J. L., Abend, R., Allen, T., Beaty, R., … Wacker, J. (under Review). Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research  on Individual Differences, PsyArXiv, 2024-7. https://doi.org/10.31219/osf.io/bjn62

Thiele, J. A., Faskowitz, J., Sporns, O., & Hilger, K. (2024). Can machine learning-based predictive modelling improve our understanding of human cognition? PNAS Nexus, 12(3), pgae519. https://doi.org/10.1093/pnasnexus/pgae519. (Direct Access Link: https://academic.oup.com/pnasnexus/article/3/12/pgae519/7915712)

Seeger, L., Kuebler, A., & Hilger, K. (2024). Drop-out rates in animal-assisted psychotherapy - results of a quantitative meta-analysis. British Journal of Clinical Psychology, 1-22. https://doi.org/10.1111/bjc.12492 

Pfeiffer, M., Kuebler, A., & Hilger, K. (2024). Modulation of Human Frontal Midline Theta by Neurofeedback: A Systematic Review and Quantitative Meta-Analysis. Neuroscience and Biobehavioral Reviews, 105696. https://doi.org/10.1016/j.neubiorev.2024.105696

Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2024). Structural-functional brain network coupling predicts human cognitive ability, Neuroimage, 120563. https://doi.org/10.1016/j.neuroimage.2024.120563  

DeYoung, C. G., Sassenberg, T., Abend, R., Allen, T., Beaty, R., Bellgrove, M., … Hilger, K., … Wacker, J. (2023). Reproducible between-person brain-behavior associations do not always require thousands of individuals. (Preprint: https://psyarxiv.com/sfnmk)

Hilger, K., Häge, A., Zedler, C., Jost, M., & Pauli, P. (2023). Virtual Reality to understand Pain-Associated Approach Behaviour: A Proof-of-Concept-Study. Scientific Reports, 13, 13799. https://rdcu.be/dkd8f

Nebe, S., Reutter, M., Baker, D., Bölte, J., Domes, G., Gamer, M., Gärtner, A., Gießing, C., Mann, C. G. née, Hilger, K., Jawinski, P., Kulke, L., Lischke, A., Markett, S., Meier, M., Merz, C., Popov, T., Puhlmann, L., Quintana, D., Schäfer, T., Schubert, A.-L., Sperl, M. F. J., Vehlen, A., Lonsdorf, T., & Feld, G. (2023). Enhancing precision in human neuroscience. eLife, 12, e85980. https://doi.org/10.7554/eLife.85980

Glück, V. M.*, Engelke, P.*, Hilger, K.*, Wong, A. H. K., Boschet, J. M. & Pittig, A. (2023). A network perspective on real-life threat, anxiety and avoidance. Journal of Clinical Psychology, 1-16. https://doi.org/10.1002/jclp.23575

Wehrheim, M. H., Faskowitz, J., Sporns, O., Fiebach, C. J., Kaschube, M., & Hilger, K. (2023). Few Temporally Distributed Brain States Predict Human Cognitive Ability. NeuroImage, 120246. https://doi.org/10.1016/j.neuroimage.2023.120246

Verona, E., Chen, H., Hall, B.,….Hilger, K.,…Clayson, P. E. (2023, in-principle acceptance, Registered Report Stage 1, Cerebral Cortex). Fear, Anxiety, and the Error-Related Negativity: A Registered Report of a Multi-Site Replication Study.

Thiele, J., Richter, A., & Hilger, K. (2023). Multimodal Brain Signal Complexity Predicts Human Intelligence. eNeuro. https://doi.org/10.1523/ENEURO.0345-22.2022

Hilger, K., & Euler, M. (2022). Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability? Journal of Cognitive Neuroscience, 35 (3), 1-17. https://doi.org/10.1162/jocn_a_01946

Kiser, D., Gromer, D., Pauli, P., & Hilger, K. (2022). A Virtual Reality Social Conditioned Place Preference Paradigm for Humans: Does Trait Social Anxiety Affect Approach and Avoidance of Virtual Agents? Frontiers in Virtual Reality, 3, 916575. https://doi.org/10.3389/frvir.2022.916575

Frischkorn, G. T.*, Hilger, K.*, Kretzschmar, A.* & Schubert, A-L.* (2022). Intelligenzdiagnostik der Zukunft: Ein Plädoyer für eine prozessorientierte und biologisch inspirierte Intelligenzmessung. Psychologische Rundschau, 73 (3), 173-189. https://doi.org/10.1026/0033-3042/a000598 (English Translation: https://psyarxiv.com/3sf7m/)

Hilger, K., Spinath, F., Troche, S. & Schubert, A-L. (2022). The Biological Basis of Intelligence: Benchmark Findings. Intelligence, 93, 101665. (Free access link: https://authors.elsevier.com/c/1fEyjaSXL~mDC)

Linhardt, M., Kiser, D., Pauli, P, & Hilger, K. (2022). Approach and Avoidance Beyond Verbal Measures: A Quantitative Meta-Analysis of Human Conditioned Place Preference Studies. Behavioural Brain Research, 113834. https://doi.org/10.1016/j.bbr.2022.113834

Thiele, J., Faskowitz, J., Sporns, O., & Hilger, K. (2022). Multi-Task Brain Network Reconfiguration is Inversely Associated with General Intelligence. Cerebral Cortex, 1-11. Free-access link: https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhab473/6523266?guestAccessKey=376a3a6e-9f15-4b27-be7a-a0e08cd6bf64

Hilger, K., & Hewig, J. (2022). Individual Differences in the Focus: Understanding Variations in Pain-Related Fear and Avoidance Behavior from the Perspective of Personality Science, PAIN, 163(2), e151-152. http://doi.org/10.1097/j.pain.0000000000002359

Hilger, K., & Sporns, O. (2021). Network Neuroscience Methods in Studying Intelligence. In A. K. Barbey, S. Kamara, & R. Haier (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience. Cambridge University Press. https://doi.org/10.1017/9781108635462

Hilger, K. & Markett, S. (2021). Personality network neuroscience: promises and challenges on the way towards a unifying framework of individual variability. Network Neuroscience, 5(2), 1-34. https://doi.org/10.1162/netn_a_00198

Hilger, K., Sassenhagen, J., Kühnhausen, J., Reuter, M. Schwarz, U., Gawrilow, C, & Fiebach, C. J. (2020). Neurophysiological markers of ADHD symptoms in typically-developing children. Scientific Reports, 10, 22460. https://doi.org/10.1038/s41598-020-80562-0

Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2020). Temporal stability of functional brain modules associated with human intelligence. Human brain mapping, 41(2), 362-372.

Hilger, K., Winter, N., Leenings, R., Sassenhagen, J., Hahn, T., Basten, U., & Fiebach, C. J. (2020). Predicting Intelligence fron Brain Gray Matter Volume. Brain Structure and Function, 225, 2111-2129. https://doi.org/10.1007/s00429-020-02113-7

Hilger, K., & Fiebach, C., J. (2019). ADHD Symptoms are Associated with the Modular Structure of Intrinsic Brain Networks in a Representative Sample of Healthy Adults. Network Neuroscience, 3(2), 567-588. https://doi.org/10.1162/netn_a_00083

Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10-25. http://doi.org/10.1016/j.intell.2016.11.001

Galeano Weber, E., Hahn, T., Hilger, K., & Fiebach, C. J. (2017). Distributed patterns of occipito-parietal functional connectivity predict the precision and variability of visual working memory. NeuroImage, 146, 404-418.

Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017). Intelligence is associated with the modular structure of intrinsic brain networks. Scientific Reports, 7(1), 1–12. https://doi.org/10.1038/s41598-017-15795-7

Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. http://doi.org/10.1016/j.intell.2015.04.009

* geteilte Erstauthorenschaft