PREreview del Odour representations supporting ethology-relevant categorisation and discrimination in the Drosophila mushroom body

This study effectively combines connectomic analysis, in vivo calcium imaging, and computational modeling to investigate how the connectivity between the 2^nd-order olfactory projection neurons (PNs) and 3^rd-order Kenyon cell (KC) of the mushroom body supports odor categorization and discrimination in Drosophila. While the prevailing view has long held that mushroom body input is random, recent connectomics data indicates that PN-to-KC connectivity may actually deviate from chance. Bridging the gap between these anatomical findings and their functional roles, the authors provide evidence that αβ and α’β’ KCs have biased connectivity toward food-related projection neurons (PNs), facilitating the categorization and potential discrimination of food odors. In contrast, γ KCs process odors more randomly without category preferences. Below are suggestions to further strengthen the conclusions.

Major Points

1.       The central conclusion is that αβ and α’β’ KCs segregate odors based on “ethological relevance” (e.g., food vs. non-food). However, the “food” odor panel consists primarily of esters, whereas the “reproduction” and “danger” panels are chemically diverse. It is difficult to determine if the observed clustering in αβ KCs results from ethological categorization or simply reflects the ability to distinguish esters. Including a more diverse range of “food” odors (e.g., ethanol, acetic acid) would strengthen the conclusion regarding ethological relevance.

2.       For Figure 4, the model parameters (spiking threshold, excitability) were optimized so that vector lengths matched experimental data. There is a risk of overfitting, which might force the model to replicate the separation observed biologically regardless of the underlying connectivity structure. Consider testing a variety of parameters to demonstrate that the connection structure, rather than parameter tuning alone, drives category separation.

3.       The network models make strong predictions about discrimination and categorization performance, but these currently lack behavioral validation. Although feasibility may be a concern, consider performing an assay where αβ/α’β’ silencing is expected to reduce such categorization between food vs non-food odors, whereas γ silencing should have smaller effects. This would further strengthen the conclusion that αβ/α’β’ KC connectivity is important for food odor discrimination.

Minor Points

1.       In Figure 1B, consider including 3D orientation axes to clearly indicate the dorsal-ventral and anterior-posterior directions.

2.       Although explained in the Methods, it would be beneficial for the authors to briefly elaborate in the main text on why Euclidean distance and Cosine distance were used in specific contexts and how they should be interpreted to improve reader comprehension.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.