Circuit asymmetries underlie functional lateralization in the mouse auditory cortex
Authors: Tiemo Marquarding
Ashlan P Reid
Christopher M Pun
Hysell V Oviedo
Nicolas Renier
Robert B Levy
Journal: Nature Communications (2019)
Discipline: Multidisciplinary
Keywords: left hemisphere dominance; social communication; lateralized cortical function; auditory cortex; mouse
Affiliations: Biology Department, The City College of New York, New York, NY, 10031, USA., Institute for Molecular and Cellular Cognition, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, 20251, Germany., The City College of New York, Macaulay Honors College, New York, NY, 10031, USA., Institut du Cerveau et de la Moelle Epini√®re, Paris, 75013, France., Biology Department, The City College of New York, New York, NY, 10031, USA. ., CUNY Graduate Center, New York, NY, 10016, USA. .

Abstract:  The left hemisphere's dominance in processing social communication has been known for over a century, but the mechanisms underlying this lateralized cortical function are poorly understood. Here, we compare the structure, function, and development of each auditory cortex (ACx) in the mouse to look for specializations that may underlie lateralization. Using Fos brain volume imaging, we found greater activation in the left ACx in response to vocalizations, while the right ACx responded more to frequency sweeps. In vivo recordings identified hemispheric differences in spectrotemporal selectivity, reinforcing their functional differences. We then compared the synaptic connectivity within each hemisphere and discovered lateralized circuit-motifs that are hearing experience-dependent. Our results suggest a specialist role for the left ACx, focused on facilitating the detection of specific vocalization features, while the right ACx is a generalist with the ability to integrate spectrotemporal features more broadly. 

Retracted: No

PubPeer comments:
 Could the authors please give more details regarding the statistical tests used on the data of Fig. 1? (p=0.05 with n=3 is, unfortunately, not strong evidence for a difference.) , 
 The entire point of statistics is to make inferences from small groups. If something is statistically significant at n=3 then it is probably quite significant indeed given any fixed power analysis.  n ing up is actually an incorrect thing to do. Moreover, a test statistic is not the be all end all of data interpretation. If the treatment effect is large and the test statistic is moderate, the observation can still be meaningful. There are many recent discussions regarding overreliance on test statistics. , 
 #2 I think you ll find that the validity of most tests is hard to establish for such small samples, and that p = 0.05 is weak evidence. I.e. some tests applied to small samples can underestimate p, while low power brings its own host of additional problems: https: garstats.wordpress.com 2017 02 04 small-sample-sizes However, that may not undermine the main conclusions of this paper, as (partially) corroborating experiments were carried out using other techniques. In any case, my question related to the actual analysis carried out. It simply isn t clear to me which test was applied and to what data. For instance, the Methods section lists several tests, without saying which was applied where. Interestingly, the above blog also has a post about comparing correlations: https: garstats.wordpress.com 2019 06 17 compindcorr The authors probably want to perform such a comparison in their Fig. 2e vs 2f. ,