Stable, multi-day, functional measurement to study cellular level plasticity in mouse primary visual cortex by fast 3D imaging
Dominika Nagy1, Andrius Plauška1, Gergely Szalay1, Máté Marosi1, Domonkos Pinke1, Csaba Csupernyák1, Alexa Bojdán1, Áron Szepesi1, Tamás Tompa1, Katalin Ócsai2, Gergely Katona1, 2, Balázs Rózsa1, 2
We investigated how 3D representation of the visual information, which is perceived and understood by the behaving animals in the primary visual cortex (V1), is changed while the animal engaged in a visual task. To reach this goal we use in vivo two-photon acusto-optic microscopy (with AAV-Syn-GCaMP6s or AAV-Syn-jRGECO1a) to record cellular responses (through different cortical layers) to visual stimuli in V1 before (baseline) and after (effect) visual training. During training period, the mice learn to discriminate visual landmarks in a virtual reality.To study cellular plasticity in time we had to measure the same neuronal ensembles (up to 200 cells) during the baseline and effect period which can be apart in time (10-20 days). As neuronal responses are very sensitive to the spatial inconsistency of recording coordinates, therefore, orientation tuning and other properties of the recorded visually evoked responses could be contaminated with recording artifacts. To resolve this critical issue, we used 3D AO drift scanning microscopy, which can extend each scanning point to small 3D line-, surface- or volume-elements. Furthermore, we are able to scan small volumes (40x40x40 µm) around each cell bodies in 3D, and use these mini-volume information to carefully and precisely realign all individual scanning regions at each measurement day, significantly reducing the chance of mismatching the recording coordinates. AO scanning of hundreds of cells at cortical depths up to 1 mm makes it feasible to examine the effect of learning in behavior experiments both at single-cell and at network scales. We found that, in contrast to previous theories, adult mice brain is plastic as the temporal dynamics of the visual representation can change at multiple temporal scales following visual learning in individual neurons.