Stabilität von funktionellen Konnektivitätsanalysen im Rahmen genetischer Bildgebung

Functional magnetic resonance imaging can be combined with genotype assessment to identify brain systems that mediate genetic vulnerability to mental disorders like schizophrenia and bipolar disorder. A data analysis approach that is widely applied is functional connectivity. In this approach, the temporal correlation between the signal from a pre-defined brain region and other brain regions is determined. In this work is shown how the choice of freely selectable data analysis parameters strongly influences the assessment of the genetic modulation of connectivity features. It is focused on three exemplary methodological parameters: (i) selection of points for the correlation analysis in the predefined brain region, (ii) methods to improve the signal-to-noise ratio, and (iii) use of additional second level covariates. Our results show that even small variations in the implementation of a functional connectivity analysis can have an impact on the connectivity pattern that is as strong as the potential modulation by genetic allele variants. Some effects of genetic variation can only be found for one specific implementation of the connectivity analysis. A reoccurring difficulty in the field of psychiatric genetics is the non-replication of initially promising findings, partly caused by the small effects of single genes. For a meaningful comparison of imaging genetics studies however, it is therefore necessary to provide more details on specific methodological parameters and to give information how robust effects are across the choice of methodological parameters.