Quantitative third-harmonic generation imaging of mouse visual cortex areas reveals correlations between functional maps and structural substrates.

The structure of brain regions is assumed to correlate with their function, but there are very few instances in which the relationship has been demonstrated in the live brain. This is due to the difficulty of simultaneously measuring functional and structural properties of brain areas, particularly at cellular resolution. Here, we performed label-free, third-harmonic generation (THG) microscopy to obtain a key structural signature of cortical areas, their effective attenuation lengths (EAL), in the vertical columns of functionally defined primary visual cortex and five adjacent visual areas in awake mice. EALs measured by THG microscopy in the cortex and white matter showed remarkable correspondence with the functional retinotopic sign map of each area. Structural features such as cytoarchitecture, myeloarchitecture and blood vessel architecture were correlated with areal EAL values, suggesting that EAL is a function of these structural features as an optical property of these areas. These results demonstrate for the first time a strong relationship between structural substrates of visual cortical areas and their functional representation maps in vivo. This study may also help in understanding the coupling between structure and function in other animal models as well as in humans.

A neural circuit model for human sensorimotor timing.

Humans and animals can effortlessly coordinate their movements with external stimuli. This capacity indicates that sensory inputs can rapidly and flexibly reconfigure the ongoing dynamics in the neural circuits that control movements. Here, we develop a circuit-level model that coordinates movement times with expected and unexpected temporal events. The model consists of two interacting modules, a motor planning module that controls movement times and a sensory anticipation module that anticipates external events. Both modules harbor a reservoir of latent dynamics, and their interaction forms a control system whose output is adjusted adaptively to minimize timing errors. We show that the model's output matches human behavior in a range of tasks including time interval production, periodic production, synchronization/continuation, and Bayesian time interval reproduction. These results demonstrate how recurrent interactions in a simple and modular neural circuit could create the dynamics needed to control timing behavior.