

Nature Neuroscience - 10, 1 (2007)

Setting the record straight



EditorialNature Neuroscience - 10, 1 (2007)

The discovery of serious errors in two recent papers in the journal leads to lessons for authors, referees and editors.





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The first correction involves a Brief Communication (Makara et al., 2005) reporting that inhibition of the enzyme that breaks down the endocannabinoid 2-arachidonoylglycerol enhances retrograde signaling in the hippocampus. The authors concluded that 2-arachidonoylglycerol is important for synaptic plasticity and that the enzyme is a possible drug target, in part because one of the putative inhibitors tested appeared to be specific for the enzyme. They subsequently discovered that

the commercial preparation of this drug was contaminated. When the contaminant was eliminated, the effect disappeared.





The second correction is more complex. The original article ( . . .The second correction is more complex. The original article ( Grill-Spector et al., 2006 ) reported high-resolution fMRI measurements in the fusiform face area (FFA), a region of the visual cortex that responds more to faces than to other visual stimuli. The authors drew two conclusions: that the FFA is heterogeneous, in that the degree of selectivity varies over the region, and—more remarkably—that the FFA contains some voxels that are highly selective for object categories other than faces. After the paper was published, two groups wrote to point out flaws in the analysis. One letter ( Simmons et al., 2007 ) noted that

the authors used a formula for selectivity that erroneously assigns high selectivity values to voxels with negative responses to nonpreferred categories, causing a substantial overestimate in selectivity for all object categories.





Another group ( Another group ( Baker et al., 2007 ) spotted a more subtle flaw:

the analysis used to demonstrate selectivity for particular categories did not distinguish between random variation and replicable effects reflecting neural tuning.

Random variation can cause some voxels to respond more to some categories than to others. To demonstrate that such differences reflect neural selectivity requires an appropriate statistical analysis, for instance cross-validation across independent datasets. The original paper seemed to report the results of such an analysis—that voxel selectivity was highly correlated between even and odd scans. However, communication with the authors revealed that

this analysis had excluded voxels whose responses were negatively correlated across the two sets of scans, a detail that was omitted from the paper.

This restriction could falsely increase consistency across scans. Indeed, when the authors redid their analysis without it,

the selectivity for nonface objects was not replicated from one set of scans to the next.