2026 ECNP Congress

PS02-2223

Mapping the multi-modal landscape of psychiatric disorders through integrative neurogenetics

Maja Rebecca Adel — L. Piszczek, V. Millischer, D. Rujescu, W. Haubensak · Medical University of Vienna, Center for Brain Research- Department of Neuronal Cell Biology, Vienna, Austria

2026 ECNP Congress

Psychiatric disorders often present substantial levels of comorbidity and shared symptoms, and are understood to be caused by an interplay of environmental factors, such as external stressors, with a biological predisposition. This biological predisposition includes genetic features, which have been shown to play an important role in the aetiology of psychiatric disorders [1], as well as impairments in structural and functional connectivity in the brain, which can also be linked to various symptoms. The high co-occurrence of psychiatric disorders can be partially explained through shared genetic features and affected pathophysiological aspects, including impairments in structural and functional connectivity. These shared and distinct pathophysiological features are typically explored within specific modalities such as psychiatric genomics or brain imaging, as efforts to combine them are rather challenging and uncommon. However, such approaches can result in unimodal perspectives, which poses the risk of overlooking latent and shared factors that could help to improve the understanding and treatment of these conditions. In this study, we address this gap by integrating multi-modal brain data to gain more holistic insights into how genetic variation influences brain activity in health and disease. For comprehensive integration and pattern finding of this multi-modal genetic and brain data space, we adopted a novel approach based on a Genetic Algorithm for Generalized Biclustering (GABi) [2]. This algorithm mines gene sets, in this case genes associated with selected psychiatric disorders, that also present high expression correlation with functional networks (FNs). This identifies networks in which genetic variation contributes to increased susceptibility and potentially resilience to psychiatric disorders. We selected FNs from publicly available sources, including the human connectome project (HPC) [3] and Neurosynth [4]. We selected 13 publicly available GWAS summary statistics of different ancestries comprising affective, compulsive, neurodevelopmental, and substance use disorders, along with six overarching genetic factors [1]. Gene-based analyses revealed 16061 genes associated with at least one psychiatric disorder or genetic factor. These genes presented a substantial overlap, with 12436 genes being associated with more than one disorder or factor. To perform the biclustering, we isolated the overlap of genes associated with different psychiatric disorders that exhibit high spatial correlations with task-specific brain activity maps and other FNs spanning various relevant domains. Of the 15438 genes associated with at least one psychiatric disorder, 14645 of which were linked to at least one FN. Out of the 9470 genes associated with at least one genetic factor, 9042 were linked to at least one FN. Performing GABi using these genes exposes pathophysiological networks that could not be identified through genetic or connectivity analyses alone, allowing for a deeper molecular to systems level exploration of these disorders. Subsequent analyses of the identified gene – FN biclusters reveal biological characteristics that increases vulnerability towards psychiatric disorders, which in the future may aid in refining diagnostic boundaries and treatment options.

References

  1. Grotzinger AD, Werme J, Peyrot WJ, Frei O, de Leeuw C, Bicks LK, et al. Mapping the genetic landscape across 14 psychiatric disorders. Nature. 2026;649:406–415.
  2. Piszczek L, Fazzari C, Ulonska S, Bühler K, Haubensak W. Computational reconstruction of evolutionary selection in human brain networks. Front Neuroinformatics. 2025;19:1623174.
  3. Barch DM, Burgess GC, Harms MP, Petersen SE, Schlaggar BL, Corbetta M, et al. Function in the human connectome: Task-fMRI and individual differences in behavior. NeuroImage. 2013;80:169–189.
  4. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8:665–670.