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 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

Symbiotic robot organisms: REPLICATOR and SYMBRION projects

Cooperation and competition among stand - alone swarm agents can increase the collective fitness of the whole system. An interesting form of collective system is demonstrated by some bacteria and fungi, which can build symbiotic organisms. Symbiotic communities can enable new functional capabilities which allow all members to survive better in their environment. In this article we show an overview of two large European projects dealing with new collective robotic systems which utilize principles derived from natural symbiosis. The paper provides also an overview of typical hardware, software and methodological challenges arose along these projects, as well as some prototypes and on-going experiments available on this stage

Sample characteristics: sex, age and education. According to their rs1006737 genotype subjects were divided into three groups (G/G, G/A, and A/A). G = Guanine, A = Adenine (risk allele).

<p>Sample characteristics: sex, age and education. According to their rs1006737 genotype subjects were divided into three groups (G/G, G/A, and A/A). G = Guanine, A = Adenine (risk allele).</p

Seed Voxel Localization.

<p>Top: Frequencies of MNI-coordinates in the X, Y, and Z dimension for right DLPFC seed regions identified with the global maximum and the next local maximum starting at 52, 30, 30. Bottom: Individual seed-region localization in the right DLPFC for the functional connectivity analyses based on the global maximum (left) and the next local maximum approach (right).</p

Influence of Noise Regression Methods on Connectivity Patterns.

<p>Functional connectivity group results (using one sample t-tests) with respect to the right DLPFC for the “standard implementation” of the connectivity analysis (top) and the additional global signal correction by application of the global normalization procedure (bottom). Yellow-red: positive correlations with seed voxel, green-blue: negative correlations with seed voxel.</p

Influence of Seed Voxel Coordinates as Covariates on the Group Level.

<p>Reduction of genotype effects after entering seed-voxel localization as covariate on the second level. Left: A linear decrease in regional activation is found with number rs1006737 risk alleles in the right DLPFC (blue). A similar gene-dosage dependent effect in functional connectivity was found at the same location (red). After controlling for differences in the spatial distribution of seed-voxel localization with introduction of the MNI coordinates in the x-, y-, and z-dimension as covariate on the second level the associations of genotype with DLPFC connectivity was reduced and non-significant at p>.05 corrected</p

Influence of Seed Voxel Localization on the Comparisons of Genetic Groups.

<p>Left: Interaction between the factors method (next local maximum vs. global maximum) and rs1006737 genotype (G/G, G/A, A/A) in the left anterior HF. Right: A post-hoc analysis of the parameter estimates of each risk group separately for both methods. A significant linear gene-dosage effect is found only with one method of seed region selection, the next local maximum approach.</p

Influence of Noise Regression Methods on the Comparisons of Genetic Groups.

<p>A linear increase with with rs1006737 genotype in functional coupling between the right DLPFC (seed voxel) and the right hippocampus is detected for both noise reduction algorithms. However, the interpretation of the changes depending on the underlying correlation profile. While in the “standard implementation” one would argue that the hippocampus is decoupled in GG carriers and that coupling increases with genetic risk (GG</p

Development of an integrated genome informatics, data management and workflow infrastructure: A toolbox for the study of complex disease genetics

Abstract The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.</p