Neural links of paranoia in a transdiagnostic sample
Poster No:
415
Submission Type:
Abstract Submission
Authors:
Maja Neidhart1, Gina-Isabelle Henze2, Karina Janson3, Nathalie Holz3, Frauke Nees4, Sylvane Desrivières5, Andre Marquand6, Gunter Schumann7, Henrik Walter8, Tobias Banaschewski9
Institutions:
1Charité Universitätsmedizin, Berlin, Berlin, 2Charité Universitätsmedizin Berlin, Berlin, Berlin, 3Central Institute of Mental Health, Mannheim, Baden-Württemberg, 4University Medical Center Schleswig-Holstein, Kiel, Schleswig-Holstein, 5King's College London, London, London, 6Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Gelderland, 7Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 8Charité–Universitätsmedizin Berlin, Berlin, Germany, 9Department of Child and Adolescent Psychiatry and Psychotherapy, Heidelberg University, Mannheim, Germany
First Author:
Co-Author(s):
Gunter Schumann
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Tobias Banaschewski
Department of Child and Adolescent Psychiatry and Psychotherapy, Heidelberg University
Mannheim, Germany
Department of Child and Adolescent Psychiatry and Psychotherapy, Heidelberg University
Mannheim, Germany
Introduction:
Paranoia is the belief that someone intends to harm you, despite no actual intent (Freeman, 2024). It is common, affecting over 10% of the general population regularly (Freeman, 2007; Neidhart et al., 2024). It could be considered as a transdiagnostic phenomenon, as it occurs in various psychiatric disorders such as schizophrenia, depression, or anxiety (Bebbington & Freeman, 2017; Byrne et al., 2015; Radaelli et al., 2014). The triple network model (TPN) of aberrant saliency mapping and cognitive dysfunction is an important model within schizophrenia research and includes the salience network (SN), default mode network (DMN), and central executive network (CEN) (B. Menon, 2019; V. Menon, 2023). The TPN postulates that an increased attention to external/internal stimuli leads to a more intense search for meaning, resulting in feelings of threat (V. Menon, 2023). Here, we examined paranoia within a transdiagnostic sample (N = 546) and its neural correlates within main TPN-hubs to better understand the etiology of paranoia as a transdiagnostic phenomenon.
Methods:
A transdiagnostic sample with N = 546 patients (including depression, bipolar disorder, schizoaffective disorder, attention deficit hyperactivity disorder, alcohol use disorder, eating disorders, schizophrenia, and psychosis) from different multi-center studies was investigated. First, behavioral data was harmonized following the protocol by Neidhart et al. (2024) resulting in a standardized paranoia score using Symptom-Checklist-90 (SCL-90) and Brief Symptom Inventory (BSI-53) as measurements and healthy controls of N = 1625 as reference cohort. Second, resting state functional connectivity (rsFC) data of N = 546 was preprocessed and analyzed using ENIGMA HALFpipe (Waller et al., 2022) and FSL 6.0 (FMRIB Software Library, Oxford, United Kingdom). Seed-based correlation analyses were performed using masks (r = 6mm) of TPN-seeds for SN (left/right insula, left/right dACC), DMN (middle posterior cingulate cortex (pcc), middle precuneus, left/right hippocampus), and CEN (left/right dorsolateral prefrontal cortex (dlPFC)). Impact of paranoia on rsFC of the TPN was investigated within group-level one sample t-tests with additional covariate (i.e., paranoia) correcting for age, sex, site and diagnosis. rsFC maps were converted using Fisher Z-transformation, FDR-correction was applied. Cluster-threshold was k > 10.
Results:
Validation checks of behavioral data harmonization showed that pre- and post-harmonization scores did not change (p > 0.01), whereby pre- and post-scores correlated highly significant with each other (p < 0.01), demonstrating successful data harmonization. Within the transdiagnostic patient sample, paranoia was significantly associated with within- and between hyperconnectivity (pFDR < 0.01, k > 10) of primary hubs of the SN (dACC), DMN (precuneus, pcc, hippocampus) and CEN (dlPFC) to other brain structures that are part of DMN, CEN, visual network (VN) and sensorimotor network (SMN). That means the higher paranoia the more connectivity in these areas. Fig. 1 visualizes hyperconnectivity between seed and connectivity region related to paranoia.
·This figure visualizes the association between seed and connectivity region related to paranoia.
Conclusions:
Key nodes of the TPN-networks (SN, DMN, CEN) showed significant (pFDR < 0.01, k > 10) between- and within hyperconnectivity to other networks, such as the SN, DMN, CEN, VN and SMN in association with paranoia. Given this is a transdiagnostic sample, it highlights the importance of TPN-connectivity for feelings of threat and addressing cognitive explanations of internal/external stimuli for the development of paranoia. Next step is to further investigate between- and within connectivity of those TPN-hubs and its relationship to paranoia in comparison with healthy controls.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
DISORDERS
Psychiatric Disorders
Schizophrenia
Other - Paranoia
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Which processing packages did you use for your study?
Provide references using APA citation style.
Byrne, G. J., Steele, S. J., & Pachana, N. A. (2015). Delusion-like experiences in older people with anxiety disorders. International Psychogeriatrics, 27(7), 1191–1196. https://doi.org/10.1017/S1041610215000113
Freeman, D. (2007). Suspicious minds: The psychology of persecutory delusions. Clinical Psychology Review, 27(4), 425–457. https://doi.org/10.1016/j.cpr.2006.10.004
Freeman, D. (2024). Understanding and Treating Persecutory Delusions. Schizophrenia Bulletin, 50(2), 233–235. https://doi.org/10.1093/schbul/sbae012
Menon, B. (2019). Towards a new model of understanding–The triple network, psychopathology and the structure of the mind. Medical Hypotheses, 133, 109385. https://doi.org/10.1016/j.mehy.2019.109385
Menon, V. (2023). 20 years of the default mode network: A review and synthesis. Neuron. https://doi.org/10.1016/j.neuron.2023.04.023
Neidhart, M., Kjelkenes, R., Jansone, K., Rehák Bučková, B., Holz, N., Nees, F., Walter, H., Schumann, G., Rapp, M. A., Banaschewski, T., Schwarz, E., Marquand, A., on behalf of the environMENTAL consortium, Heinz, A., Ralser, M., Twardziok, S., Vaidya, N., Bernas, A., Serin, E., … Ogoh, G. (2024). A protocol for data harmonization in large cohorts. Nature Mental Health, 2(10), 1134–1137. https://doi.org/10.1038/s44220-024-00315-0
Neidhart, M., Mohnke, S., Vogel, B. O., & Walter, H. (2024). The architecture of paranoia in the general population: A self-report and ecological momentary assessment study. Schizophrenia Research, 271, 206–219. https://doi.org/10.1016/j.schres.2024.07.021
Radaelli, D., Poletti, S., Gorni, I., Locatelli, C., Smeraldi, E., Colombo, C., & Benedetti, F. (2014). Neural correlates of delusion in bipolar depression. Psychiatry Research: Neuroimaging, 221(1), 1–5. https://doi.org/10.1016/j.pscychresns.2013.10.004
Waller, L., Erk, S., Pozzi, E., Toenders, Y. J., Haswell, C. C., Büttner, M., Thompson, P. M., Schmaal, L., Morey, R. A., Walter, H., & Veer, I. M. (2022). ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting‐state and task‐based fMRI data. Human Brain Mapping, 43(9), 2727–2742. https://doi.org/10.1002/hbm.25829