2026 ECNP Congress

PS03-3167

Mapping the molecular foundations of neural network alterations in bipolar disorder

Stefanie Fischer — M. Gruber, F. Timm, P. Rehm, H. Staub, L. Krätzig, D. Schepers, A. Reif, T. Kircher, U. Dannlowski, C. Ecker, J. Repple · Department of Psychiatry- Psychosomatic Medicine and Psychotherapy, University Hospital- Goethe University Frankfurt, Frankfurt, Germany

2026 ECNP Congress

Introduction

Bipolar disorder (BD) is a severe, highly heritable, and polygenic affective disorder characterized by recurrent mood episodes and substantial psychosocial impairment. BD is increasingly understood as a disorder of large-scale neural network dysconnectivity, potentially driven by synaptic, mitochondrial, immune, and circadian pathways. By integrating connectomics and transcriptomics, this study examines how polygenic risk and regional gene expression shape brain network architecture. Focusing on healthy controls (HC), we isolate these molecular influences on structural connectivity from clinical confounds such as medication and disease burden to establish a mechanistic framework linking genetic liability and network-level pathology, ultimately aiming to identify biological targets for more effective prognosis and treatment. We hypothesize that higher polygenic risk for BD (PRSBD) is significantly correlated with altered nodal connectivity strength in HC and that spatial patterns of PRSBD–connectome associations align with regional gene-expression profiles.

Methods

Participants were drawn from of the Marburg–Münster Affective Disorders Cohort Study [1]. The sample comprised 1075 HCs (mean age = 35,2 ± 13,5 years, range = 18 – 65; 64,9 % female). Exclusion criteria included current or previous psychiatric or neurological disorders, verbal IQ < 80, significant head trauma, or current benzodiazepine use. High-resolution T1-weighted and diffusion-weighted MRI data were acquired at 3 T scanners in Marburg and Münster using harmonized protocols. Structural brain networks were reconstructed via deterministic tractography using the Cammoun subdivision of the FreeSurfer’s Desikan–Killiany atlas, as implemented in the CATO toolbox, yielding weighted connectivity matrices undergoing rigorous quality control [2]. Genome-wide genotyping with imputation to the 1000 Genomes Phase 3 reference generated an unrelated genetic subsample, from which a single polygenic risk score for BD (PRSBD) was derived [3]. Gene expression profiles were obtained from the Allen Human Brain Atlas (AHBA) [4].

Planned Analyses: Analyses are currently ongoing. Regional connectivity analysis is performed by regressing node-wise connectivity strength onto PRSBD scores in HC using robust linear models adjusted for age, sex, scanner site and first ten principal components. Resulting node-wise t-values are mapped on the cortical surface. For the imaging–transcriptomics analysis, AHBA samples are mapped to the cortical parcellation and processed with the abagen toolbox to create a region-by-gene matrix aligned with the 114 cortical nodes. For each node, PRSBD-related t-values will be correlated across regions with gene-wise expression profiles to obtain gene–phenotype association maps, while controlling for spatial autocorrelation via permutations tests, following Ecker et al. [5]. Significant gene sets then undergo enrichment analyses, e.g. Gene Ontology, cell-type markers, and neurotransmitter systems, to identify molecular pathways and cell classes linked to BD-related network alterations and PRSBD effects.

Conclusions

The study may support a model in which polygenic liability and regional transcriptional architecture jointly shape large-scale structural dysconnectivity. In clinical terms, the findings could help identify systems-level targets for stratification and personalized intervention.

References

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