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Data from: Specific expansion of motor cortical projections in a singing mouse

Isko, Emily1; Harpole, Clifford1; Zheng, Xiaoyue Mike1; Zhan, Huiqing1; Davis, Martin1; Zador, Anthony1; Banerjee, Arkarup1

Research facility: Cold Spring Harbor Laboratory
Published Mar 30, 2026 on Dryad. https://doi.org/10.5061/dryad.8kprr4z2p

Data files

Mar 30, 2026 version files 19.08 MB

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Abstract

Data and analysis for Isko et al., 2026. Behavioral data and analysis as well as MAPseq data and analysis included.

Elucidating how modifications in neural circuit architecture drive behavioral innovation remains a key challenge in neuroscience and evolutionary biology. In mammals, the neocortex is posited to play a critical role in facilitating rapid behavioral innovations, such as language. Although changes in long-range connectivity have been proposed to underlie such innovations, these hypotheses largely remain untested quantitatively, partly due to the lack of high-throughput neuronal projection data at single-neuron resolution across species. To address this, we studied the Alston’s singing mouse (Scotinomys teguina), which exhibits a striking vocal behavior absent in the laboratory mouse (Mus musculus), to quantitatively determine species-specific changes in motor cortical projections throughout the brain. We used bulk tracing, serial two-photon tomography, and high-throughput DNA sequencing of over 76,000 barcoded neurons to discover a specific and substantial expansion (~3 fold) of orofacial motor cortical (OMC) projections to the auditory cortical region (AudR) and the midbrain periaqueductal gray (PAG), both implicated in vocal behaviors. Moreover, analysis of individual OMC neurons’ projection motifs revealed preferential expansion of exclusive projections to AudR in the singing mouse. Our results suggest that selective expansion of ancestral motor cortical projections may lead to behavioral divergence over short timescales, allowing mechanistic investigations of enhanced cortical control over vocalizations — a crucial preadaptation for human language. This approach of comparing recently-diverged species with drastic behavioral divergences can be readily generalized across other model clades to discover quantitative rules of neural circuit evolution.