Fast z-focus controlling and multiplexing strategies for multiplane two-photon imaging of neural dynamics.

Monitoring neural activity and associating neural dynamics with the anatomical connectome are required to understand how the brain works. Neural dynamics are measured by electrophysiology and optical imaging. Since the discovery of the two-photon excitation phenomenon, significant progress has been made in deep imaging for capturing neural activity from numerous neurons in vivo. The development of two-photon microscopy is aimed to image neural activity from a large and deep region with high spatial (x, y, and z) and temporal (t) resolutions at a high signal-to-noise ratio. Imaging deep regions along the optical axis (z-axis) is particularly challenging because heterogeneous biological tissues scatter and absorb light. Recent advances in the light focus modulation technology at high speeds in three dimensions (x, y, and z) have allowed multiplane two-photon imaging. z-Focus control by varifocal optical systems, such as ferroelectric liquid lenses, gradient refractive index lenses, and adaptive optical element systems, and multiplexing by time- and wavelength-division strategies have allowed to rapidly observe specimens at different focal depths. Herein, we overview the recent advances in multiplane functional imaging systems that enable four-dimensional (x, y, z, and t) analysis of neural dynamics, with a special emphasis on z-scanning mechanisms and multiplexing strategies.

Cell type- and layer-specific convergence in core and shell neurons of the dorsal lateral geniculate nucleus.

Over 40 distinct types of retinal ganglion cells (RGCs) generate parallel processing pathways in the visual system. In mice, two subdivisions of the dorsal lateral geniculate nucleus (dLGN), the core and the shell, organize distinct parallel channels to transmit visual information from the retina to the primary visual cortex (V1). To investigate how the dLGN core and shell differentially integrate visual information and other modalities, we mapped synaptic input sources to each dLGN subdivision at the cell-type level with G-deleted rabies viral vectors. The monosynaptic circuit tracing revealed that dLGN core neurons received inputs from alpha-RGCs, Layer 6 neurons of the V1, the superficial and intermediate layers of the superior colliculus (SC), the internal ventral LGN, the lower layer of the external ventral LGN (vLGNe), the intergeniculate leaf, the thalamic reticular nucleus (TRN), and the pretectal nucleus (PT). Conversely, shell neurons received inputs from alpha-RGCs and direction-selective ganglion cells of the retina, Layer 6 neurons of the V1, the superficial layer of the SC, the superficial and lower layers of the vLGNe, the TRN, the PT, and the parabigeminal nucleus. The present study provides anatomical evidence of the cell type- and layer-specific convergence in dLGN core and shell neurons. These findings suggest that dLGN core neurons integrate and process more multimodal information along with visual information than shell neurons and that LGN core and shell neurons integrate different types of information, send their own convergent information to discrete populations of the V1, and differentially contribute to visual perception and behavior.