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Kohitij Kar, Martin Schrimpf, Kailyn Schmidt, James J DiCarlo; Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision 2021;21(9):2489. doi: https://doi.org/10.1167/jov.21.9.2489.
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© ARVO (1962-2015); The Authors (2016-present)
Distributed activity patterns across multiple brain areas (e.g., V4, IT) enable primates to accurately identify visual objects. To strengthen our inferences about the causal role of underlying brain circuits, it is necessary to develop targeted neural perturbation strategies that enable discrimination amongst competing models. To probe the role of area V4 in core object recognition, we expressed inhibitory DREADDs in neurons within a 5x5 mm subregion of V4 cortex via multiple viral injections (AAV8-hSyn-hM4Di-mCherry; two macaques). To assay for successful neural suppression, we recorded from a multi-electrode array implanted over the transfected V4. We also recorded from multi-electrode arrays in the IT cortex (the primary feedforward target of V4), while simultaneously measuring the monkeys’ behavior during object discrimination tasks. We found that systemic (intramuscular) injection of the DREADDs activator (CNO) produced reversible reductions (~20%) in image-evoked V4 responses compared to the control condition (saline injections). Monkeys showed significant behavioral performance deficits upon CNO injections (compared to saline), which were larger when the object position overlapped with the RF estimates of the transfected V4 neurons. This is consistent with the hypothesis that the suppressed V4 neurons are critical to this behavior. Furthermore, we observed commensurate deficits in the linearly-decoded estimates of object identity from the IT population activity (post-CNO). To model the perturbed brain circuitry, we used a primate brain-mapped artificial neural network (ANN) model (CORnet-S) that supports object recognition. We “lesioned” the model’s corresponding V4 subregion by modifying its weights such that the responses matched a subset of our experimental V4 measurements (post-CNO). Indeed, the lesioned model better predicted the measured (held-out) V4 and IT responses (post-CNO), compared to the model's non-lesioned version, validating our approach. In the future, our approach allows us to discriminate amongst competing mechanistic brain models, while the data provides constraints to guide more accurate alternatives.
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