FB2026_03 , released September 17, 2026
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Citation
Cheng, L.S., Charng, C.C., Chen, R.H., Feng, K.L., Chiang, A.S., Lo, C.C., Lee, T.K. (2025). Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila.  Sci. Adv. 11(22): eadq9893.
FlyBase ID
FBrf0262525
Publication Type
Research paper
Abstract
In Drosophila melanogaster, olfactory encoding in the mushroom body (MB) involves thousands of Kenyon cells (KCs) processing inputs from hundreds of projection neurons (PNs). Recent data challenge the notion of random PN-to-KC connectivity, revealing preferential connections between food-related PNs and specific KCs. Our study further uncovers a broader picture-an L-shaped hybrid network, supported by spatial patterning: Food-related PNs diverge across KC classes, whereas pheromone-sensitive PNs converge on γ KCs. α/β KCs specialize in food odors, whereas γ KCs integrate diverse inputs. Such spatial arrangement extends further to the antennal lobe (AL) and lateral horn (LH), shaping a systematic olfactory landscape. Moreover, our functional validations align with computational predictions of KC odor encoding based on the hybrid connectivity, correlating PN-KC activity with behavioral preferences. In addition, our simulations showcase the network's augmented sensitivity and precise discrimination abilities, underscoring the computational benefits of this hybrid architecture in olfactory processing.
PubMed ID
PubMed Central ID
PMC12124391 (PMC) (EuropePMC)
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Secondary IDs
    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    Sci. Adv.
    Title
    Science advances
    ISBN/ISSN
    2375-2548
    Data From Reference
    Alleles (3)
    Genes (2)
    Insertions (1)
    Transgenic Constructs (2)