Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation.
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 appro...
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2011
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oai:doaj.org-article:bd6238b066334c00bf3b93528662a6c62021-11-18T07:31:19ZFunctional connectivity analyses in imaging genetics: considerations on methods and data interpretation.1932-620310.1371/journal.pone.0026354https://doaj.org/article/bd6238b066334c00bf3b93528662a6c62011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22220190/?tool=EBIhttps://doaj.org/toc/1932-6203Functional 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.Johannes BedenbenderFrieder M PaulusSören KrachMartin PykaJens SommerAxel KrugStephanie H WittMarcella RietschelDavide LaneriTilo KircherAndreas JansenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e26354 (2011) |
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Medicine R Science Q 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 connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| description |
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. |
| format |
article |
| author |
Johannes Bedenbender Frieder M Paulus Sören Krach Martin Pyka Jens Sommer Axel Krug Stephanie H Witt Marcella Rietschel Davide Laneri Tilo Kircher Andreas Jansen |
| author_facet |
Johannes Bedenbender Frieder M Paulus Sören Krach Martin Pyka Jens Sommer Axel Krug Stephanie H Witt Marcella Rietschel Davide Laneri Tilo Kircher Andreas Jansen |
| author_sort |
Johannes Bedenbender |
| title |
Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| title_short |
Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| title_full |
Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| title_fullStr |
Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| title_full_unstemmed |
Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| title_sort |
functional connectivity analyses in imaging genetics: considerations on methods and data interpretation. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2011 |
| url |
https://doaj.org/article/bd6238b066334c00bf3b93528662a6c6 |
| work_keys_str_mv |
AT johannesbedenbender functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT friedermpaulus functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT sorenkrach functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT martinpyka functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT jenssommer functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT axelkrug functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT stephaniehwitt functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT marcellarietschel functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT davidelaneri functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT tilokircher functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation AT andreasjansen functionalconnectivityanalysesinimaginggeneticsconsiderationsonmethodsanddatainterpretation |
| _version_ |
1718423373345718272 |