On 2017 Dec 21, Christa Müller-Axt commented:
We here respond to the questions and concerns raised by Franck Ramus (23th of Nov 2017). We organize our response around four topics: (i) sample size and statistical power, (ii) interpretation of effect sizes, (iii) specific predictions and correlation analyses, and (iv) current status and future directions.
(i) Sample size and statistical power: Franck Ramus argues that our study lacks statistical power due to the modest sample size (i.e., n=12 dyslexics and n=12 matched controls). First, as recommended by Button et al. (2013), we have explicitly acknowledged and discussed the modest sample size in our manuscript (see pp. 3694 & 3696). Second, given the well-known statistical problems associated with modest sample sizes, we took measures to reproduce our results in additional analyses, as laid out in the paper (i.e., surface-based analyses, see pp. 3694 & e3). While we are aware that these analyses do not constitute an independent replication, they further provide confidence in our main results, as they make it unlikely that the findings are merely due to methodological specificities. Third, the Button et al. (2013) paper elicited a more detailed discussion on sample size issues and neither Button et al. (2013) themselves nor any of the comments and papers in response to Button et al. (2013) suggested that small sample size studies should be dismissed altogether (see e.g., Bacchetti P., 2013; Quinlan P.T., 2013 for a more detailed discussion of these arguments).
(ii) Interpretation of effect sizes: Franck Ramus questions the plausibility of our results, specifically because the observed effect size for the group difference in left V5/MT-LGN connectivity is larger (d=1.28) than the observed effect size for the associated behavioral difference in rapid automatized naming (RAN) for letters and numbers (composite score: d=1.16). We were surprised by this remark. A larger effect size for left V5/MT-LGN connectivity differences than for behavioral differences on RAN could be explained by a number of reasons, e.g.: 1) altered V5/MT-LGN connectivity might also affect other cognitive processes that are not captured by RAN measures, 2) RAN is supported by additional brain networks, other than V5/MT-LGN connectivity alone, that contribute to task performance, 3) recruitment of compensatory mechanisms that partially substitute altered V5/MT-LGN connectivity to enable RAN performance. In our view, all of these mechanisms (as well as their combinations) seem plausible given the complex and nonlinear nature of the human brain and given the multi-component nature of the RAN. Therefore, we do not see how the apparent difference in reported effect sizes between the behavioral and neural measures would undermine the plausibility of our results.
(iii) Specific predictions and correlation analyses: Franck Ramus asks whether we had specific predictions about the correlations reported in our paper. We had indeed specific predictions about the RAN and reading comprehension scores. As described in the paper (pp. 3693 & 3695), these predictions were based on a previous finding from our research group that only these two scores correlated with sensory thalamus function in dyslexics in an fMRI study (Diaz et al., 2012). The correlation in that study was only found for RAN and reading comprehension in dyslexics, but not in controls (Diaz et al., 2012). Hence, in the present paper we tested the specific hypothesis that RAN and reading comprehension scores are related to V5/MT-LGN connectivity in dyslexics.
Franck Ramus states that there are other core symptoms of dyslexia. He asks whether we tested correlations of V5/MT-LGN connectivity with more behavioral measures than reported, without appropriately correcting for multiple tests. We agree that dyslexia also encompasses other core symptoms (e.g., spelling and reading speed deficits). We did, however, not test for correlations with these or other behavioral measures, as we did not have specific predictions about them. It should go without saying that we do not do selective reporting of test results. Our predictions about the correlations with RAN and reading comprehension related to the dyslexia group, while we did not expect a correlation in the control group. We therefore consider it valid to apply a Bonferroni correction for the two tested correlations within each group separately. We did not compute correlations across the whole sample of dyslexics and controls together, as this would not have addressed our hypothesis (i.e., that RAN and reading comprehension scores are related to V5/MT-LGN connectivity in individuals with developmental dyslexia).
(iv) Current status and future directions: We appreciate that Franck Ramus highlighted the issue of the replication crisis. Indeed, while we think it is important not to dismiss results merely based on sample size, we agree that novel research findings necessitate replication. For this reason, our research lab is currently conducting a large scale follow-up study for replication as well as testing of further hypotheses generated by the present findings. We hope that the present study will inspire further research on potential deficits at the level of the sensory thalami and cortico-thalamic connections in the dyslexia field – a field that is currently dominated by an almost exclusive cerebral-cortex-centric view.
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365‑376. https://doi.org/10.1038/nrn3475
Bacchetti, P. (2013). Small sample size is not the real problem. Nature Reviews Neuroscience, 14(8), 585-585. https://doi.org/10.1038/nrn3475-c3
Quinlan, P. T. (2013). Misuse of power: in defence of small-scale science. Nature Reviews Neuroscience, 14(8), 585-585. https://doi.org/10.1038/nrn3475-c1
Díaz, B., Hintz, F., Kiebel, S. J., & von Kriegstein, K. (2012). Dysfunction of the auditory thalamus in developmental dyslexia. Proceedings of the National Academy of Sciences, 109(34), 13841-13846. https://doi.org/10.1073/pnas.1119828109
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