DYNAMIC

Through DYNAMIC, interdisciplinary collaboration will leverage current network analysis methods and develop new ones to understand mental illnesses as dynamic network dysfunctions. This involves employing sophisticated analytical approaches such as nonlinear associations, dynamic Bayesian networks, and higher-order interdependencies. Machine learning will optimize prediction precision and computational efficiency, and causality analysis methods tailored to DYNAMIC will be applied.

DYNAMIC

Mental illnesses have immense health and economic impacts. However, existing classification systems hinder clinical and scientific advancements by focusing on singular symptoms without considering their interactions and change processes. To better understand these disorders, it’s crucial to view them as dynamic networks of psychopathological, psychological, and biological processes.

This approach, termed the network approach to mental disorders, considers individual symptoms, psychological processes, and local neurobiological processes as interconnected nodes in an associative network.

DYNAMICS is a research center that brings together scientists from Experimental Psychology, Clinical Psychology, Psychiatry and Artificial Intelligence in order to understand how successful interventions can be planned based on multiple empirical measurements, integrating biological and psychological processes into individualized network models.

The long-term goal is to create better patient outcomes. Therapies will be analyzed, optimized, and supplemented with new interventions based on their effects on network dynamics. Similar to personalized oncology, individualized intervention strategies based on network theories will be derived, aiming to sequence interventions optimally for the best treatment effects.

The TNM Lab is specifically involved in

Z-project activities

AP2 Longtudinal assessments

People

Prof. Dr. Benjamin Straube (PI)

Katrin Leinweber (ECR, staff)

Dr. Isabelle Ridderbusch (associate)

Dr. Yifei He (associate)

Javaheripour, N., Colic, L., Opel, N., Li, M., Balajoo, S.M., Chand, T., Maleki Balajoo, S., Chand, T., Van der Meer, J., Krylova, M., Izyurov, I., Meller, T., Goltermann, J., Winter, N.R., Meinert, S., Grotegerd, D., Jansen, A., Alexander, N., Usemann, P., Thomas-Odenthal, F., Evermann, U., Wroblewski, A., Brosch, K., Stein, F., Hahn, T., Straube, B., Krug, A., Nenadić, I., Kircher, T., Croy, I., Dannlowski, U., Wagner, G., & Walter, M., (2023). Altered brain dynamic in major depressive disorder: state and trait features. Transl. Psychiatry 13, 261. https://doi.org/10.1038/s41398-023-02540-0

Kirchner, L., Kube, T., Berg, M., Eckert, A.-L., Straube, B., Endres, D., Rief, W. (2024). Social expectations in depression. Nat. Rev. Psychol. https://doi.org/10.1038/s44159-024-00386-x

Polner, B.K., Jamalabadi, H., van Kemenade, B.M., Billino, J., Kircher, T. Straube, B. (accepted). Speech-gesture matching and schizotypal traits: A network approach. Schizophrenia Bulletin

Schneider, K., Leinweber, K., Jamalabadi, H., Teutenberg, L., Brosch, K., Pfarr, J.-K., Thomas-Odenthal, F., Usemann, P., Wroblewski, A., Straube, B., Alexander, N., Nenadić, I., Jansen, A., Krug, A., Dannlowski, U., Kircher, T., Nagels, A., &Stein, F., (2023). Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders. Schizophr. 2023 91 9, 1–10. https://doi.org/10.1038/s41537-023-00359-8

Riedl, L., Nagels, A., Sammer, G., Choudhury, M., Nonnenmann, A., Sütterlin, A., Feise, C., Haslach, M., Bitsch, F., & Straube, B. (2022). A Novel Multimodal Speech-Gesture Training and Its Impact on Quality of Life and Neural Processing in Patients With Schizophrenia Spectrum Disorder. A Pilot Randomized Controlled Trial. Schizophrenia Research, 246, 112–125

Ridderbusch, I. C., Wroblewski, A., Yang, Y., Richter, J., Hollandt, M., Hamm, A. O., … Kircher, T. … Straube, B. (2021). Neural adaptation of cingulate and insular activity during delayed fear extinction: A replicable pattern across assessment sites and repeated measurements. NeuroImage, 118157. https://doi.org/https://doi.org/10.1016/j.neuroimage.2021.118157

Wroblewski, A., He, Y., & Straube, B. (2020). Dynamic Causal Modelling suggests impaired effective connectivity in patients with schizophrenia spectrum disorders during gesture-speech integration. Schizophr. Res. in press. https://doi.org/https://doi.org/10.1016/j.schres.2019.12.005

Kirchner, L., Eckert, A.-L., Berg, M., Endres, D., Straube, B., Rief, W., (2024). An Active Inference Approach to interpersonal differences in depression. New Ideas Psychol. 74, 101092. https://doi.org/10.1016/j.newideapsych.2024.101092