MAOA‐VNTR genotype affects structural and functional connectivity in distributed brain networks. Issue 18 (23rd August 2019) - Harneit, Anais; Braun, Urs; Geiger, Lena S.; Zang, Zhenxiang; Hakobjan, Marina; van Donkelaar, Marjolein M. J.; Schweiger, Janina I.; Schwarz, Kristina; Gan, Gabriela; Erk, Susanne; Heinz, Andreas; Romanczuk‐Seiferth, Nina; et al.
Abstract: Previous studies have linked the low expression variant of a variable number of tandem repeat polymorphism in the monoamine oxidase A gene ( MAOA‐L ) to the risk for impulsivity and aggression, brain developmental abnormalities, altered cortico‐limbic circuit function, and an exaggerated neural serotonergic tone. However, the neurobiological effects of this variant on human brain network architecture are incompletely understood. We studied healthy individuals and used multimodal neuroimaging (sample size range: 219–284 across modalities) and network‐based statistics (NBS) to probe the specificity of MAOA‐L ‐related connectomic alterations to cortical‐limbic circuits and the emotion processing domain. We assessed the spatial distribution of affected links across several neuroimaging tasks and data modalities to identify potential alterations in network architecture. Our results revealed a distributed network of node links with a significantly increased connectivity in MAOA‐L carriers compared to the carriers of the high expression (H) variant. The hyperconnectivity phenotype primarily consisted of between‐lobe ("anisocoupled") network links and showed a pronounced involvement of frontal‐temporal connections. Hyperconnectivity was observed across functional magnetic resonance imaging (fMRI) of implicit emotion processing ( p FWE = .037), resting‐state fMRI ( p FWE = .022), and diffusion tensor imaging ( p FWE = .044) data, while no effects were seen in fMRI data of another cognitive domain, that is, spatial working memory ( p FWE = .540). These observations are in line with prior research on the MAOA‐L variant and complement these existing data by novel insights into the specificity and spatial distribution of the neurogenetic effects. Our work highlights the value of multimodal network connectomic approaches for imaging genetics.