Searching for behavior relating to grey matter volume in a-priori defined right dorsal premotor regions: Lessons learned

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Recently, we showed that the functional heterogeneity of the right dorsal premotor (PMd) cortex could be better understood by dividing it into five subregions that showed different behavioral associations according to task-based activations studies. The present study investigated whether the revealed behavioral profile could be corroborated and complemented by a structural brain behavior correlation approach in two healthy adults cohorts.Grey matter volume within the five volumes of interest (VOI-GM) was computed using voxel-based morphometry. Associations between the inter-individual differences in VOI-GM and performance across a range of neuropsychological tests were assessed in the two cohorts with and without correction for demographical variables. Additional analyses were performed in random smaller subsamples drawn from each of the two cohorts.In both cohorts, correlation coefficients were low; only few were significant and a considerable number of correlations were counterintuitive in their directions (i.e., higher performance related to lower grey matter volume). Furthermore, correlation patterns were inconsistent between the two cohorts. Subsampling revealed that correlation patterns could vary widely across small samples and that negative correlations were as likely as positive correlations.Thus, the structural brain-behavior approach did not corroborate the functional profiles of the PMd subregions inferred from activation studies, suggesting that local recruitment by fMRI studies does not necessarily imply covariance of local structure with behavioral performance in healthy adults. We discuss the limitations of such studies and related recommendations for future studies.HighlightsCorrelations between grey matter volume in PMd and behavioral scores were performed.The correlation patterns show poor reliability across two cohorts.Correlation coefficients are low and negative correlations are frequent.Low effect sizes in noisy data possibly reflect spurious findings.

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