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Related Experiment Video

Updated: May 26, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Functional connectivity analyses in imaging genetics: considerations on methods and data interpretation.

Johannes Bedenbender¹, Frieder M Paulus, Sören Krach

¹Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany.

|January 6, 2012

Summary

This summary is machine-generated.

Methodological choices in functional magnetic resonance imaging (fMRI) analysis significantly impact imaging genetics findings. Ensuring robust and replicable results requires detailed reporting of analysis parameters in functional connectivity studies.

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Last Updated: May 26, 2026

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Neuroscience
Genetics
Psychiatry

Background:

Imaging genetics combines functional magnetic resonance imaging (fMRI) with genotype data to study genetic influences on mental disorders.
Functional connectivity analysis, assessing temporal correlations in fMRI signals, is a common approach in imaging genetics.
Replication of findings in psychiatric genetics is challenging due to small genetic effects and methodological variability.

Purpose of the Study:

To investigate how variations in data analysis parameters affect the assessment of genetic modulation in functional connectivity.
To highlight the critical need for detailed methodological reporting to ensure the comparability and replicability of imaging genetics studies.

Main Methods:

Focused on functional connectivity analysis using fMRI data.
Examined the influence of three key methodological parameters: seed voxel selection, noise reduction algorithms, and the use of covariates.
Assessed the impact of these parameters on the detection of genetic effects on brain connectivity.

Main Results:

Variations in analysis parameters, such as seed voxel selection, can exert an influence on connectivity patterns comparable to genetic effects.
Some genetic effects on connectivity were only detectable with specific analytical implementations.
The choice of methodological parameters significantly impacts the observed genetic modulation of functional connectivity.

Conclusions:

Methodological parameter choices in functional connectivity analysis can obscure or reveal genetic effects, impacting the reliability of imaging genetics findings.
Detailed reporting of seed voxel selection, noise reduction, and covariate usage is essential for study replication and comparison.
Standardizing and transparently reporting analytical methods are crucial for advancing the field of imaging genetics and understanding genetic contributions to mental disorders.