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Tonotopic organization of auditory cortex in awake marmosets revealed by multi-modal wide-field optical imaging


Tonotopic organization of the auditory cortex has been extensively studied in many mammalian species using various methodologies and physiological preparations. Tonotopy mapping in primates, however, is more limited due to constraints such as cortical folding, use of anesthetized subjects, and mapping methodology. Here we applied a combination of through-skull and through-window intrinsic optical signal imaging, wide-field calcium imaging, and neural probe recording techniques in awake marmosets (Callithrix jacchus), a New World monkey with most of its auditory cortex located on a flat brain surface. Coarse tonotopic gradients, including a recently described rostral-temporal (RT) to parabelt gradient, were revealed by the through-skull imaging of intrinsic optical signals and were subsequently validated by single-unit recording. Furthermore, these tonotopic gradients were observed with more detail through chronically implanted cranial windows with additional verifications on the experimental design. Moreover, the tonotopy mapped by the intrinsic-signal imaging methods was verified by wide-field calcium imaging in an AAV-GCaMP labeled subject. After these validations and with further effort to expand the field of view more rostrally in both windowed and through-skull subjects, an additional putative tonotopic gradient was observed more rostrally to the area RT, which has not been previously described by the standard model of tonotopic organization of the primate auditory cortex. Together, these results provide the most comprehensive data of tonotopy mapping in an awake primate species with unprecedented coverage and details in the rostral proportion and support a caudal-rostrally arranged mesoscale organization of at least three repeats of functional gradients in the primate auditory cortex, similar to the ventral stream of primate visual cortex.


Xindong Song, Yueqi Guo, Chenggang Chen, Jong Hoon Lee,  Xiaoqin Wang

Published: 2024

PMID: 38799765



Research Area:

Systems Neuroscience, Methodological Studies, Computational Neuroscience