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PLoS By Category | Recent
PLoS Articles
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Mathematics - Mental Health - Neurological Disorders - Neuroscience - Physiology - Science Policy
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Functional Connectivity Analyses in Imaging Genetics: Considerations on Methods and Data Interpretation
Published:
Thursday, December 29, 2011
Author:
Johannes Bedenbender et al.
by Johannes Bedenbender, Frieder M. Paulus, Sören Krach, Martin Pyka, Jens Sommer, Axel Krug, Stephanie H. Witt, Marcella Rietschel, Davide Laneri, Tilo Kircher, Andreas Jansen
Functional magnetic resonance imaging (fMRI) can be combined with genotype assessment to identify brain systems that mediate genetic vulnerability to mental disorders (“imaging genetics”). A data analysis approach that is widely applied is “functional connectivity”. In this approach, the temporal correlation between the fMRI signal from a pre-defined brain region (the so-called “seed point”) and other brain voxels is determined. In this technical note, we show how the choice of freely selectable data analysis parameters strongly influences the assessment of the genetic modulation of connectivity features. In our data analysis we exemplarily focus on three methodological parameters: (i) seed voxel selection, (ii) noise reduction algorithms, 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. The replication of imaging genetic results is therefore crucial for the long-term assessment of genetic effects on neural connectivity parameters. For a meaningful comparison of imaging genetics studies however, it is therefore necessary to provide more details on specific methodological parameters (e.g., seed voxel distribution) and to give information how robust effects are across the choice of methodological parameters.
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