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Acar, F.

Publications and source records attributed to Acar, F..

2 recordsLinked to original sources

Improving reliability in clinical neuroimaging: a study in transgender persons

Although the neuroanatomy of transgender persons is slowly being charted, findings are presently discrepant. One important factor is the issue of power and low signal-to-noise (SNR) ratio in neuroimaging studies of rare study populations including endocrine or neurological patient groups. The present study assessed whether the reliability of findings across structural anatomical measures including thickness, volume, and surface area could be increased by using two back-to-back within session structural MRI scans in 40 transgender men (TM), 40 transgender women (TW), 30 cisgender men (CM), and 30 cisgender women (CW). Overall, findings in transgender persons were more consistent with at-birth assigned sex in brain volume and surface area while no group differences emerged for cortical thickness. Repeated measures analysis also indicated that having a second scan increased SNR in all ROIs, most notably bilateral frontal poles, accumbens nuclei and putamina. Furthermore, additional significant group differences emerged in cortical surface area when age and ICV were used as covariates. The results suggest that a simple time and cost effective measure to improve signal to noise ratio in rare clinical populations with low prevalence rates is a second anatomical scan when structural MRI is of interest.

neuroscience↗

Assessing robustness against potential publication bias in coordinate based fMRI meta-analyses using the Fail-Safe N

The importance of integrating research findings is incontrovertible and coordinate based meta-analyses have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. Similar to classical meta-analyses, coordinate based meta-analyses may be subject to different forms of publication bias which impacts results and possibly invalidates findings. We develop a tool that assesses the robustness to potential publication bias on cluster level. We investigate the possible influence of the file-drawer effect, where studies that do not report certain results fail to get published, by determining the number of noise studies that can be added to an existing fMRI meta-analysis before the results are no longer statistically significant. In this paper we illustrate this tool through an example and test the effect of several parameters through extensive simulations. We provide an algorithm for which code is freely available to generate noise studies and enables users to determine the robustness of meta-analytical results.

neuroscience↗