Improved green and red GRAB sensors for monitoring dopaminergic activity in vivo.

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a large network of dopaminergic projections. To dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent G-protein-coupled receptor activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity and signal-to-noise ratio with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

Improved dual-color GRAB sensors for monitoring dopaminergic activity in vivo.

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a vast network of dopaminergic projections. To fully dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent GPCR activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity, and signal-to-noise properties with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in freely moving mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala, and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

Optimal linear estimation under unknown nonlinear transform.

Linear regression studies the problem of estimating a model parameter ß* ∈ℝ p , from n observations [Formula: see text] from linear model yi = 〈xi , ß*〉 + ε i . We consider a significant generalization in which the relationship between 〈xi , ß*〉 and yi is noisy, quantized to a single bit, potentially nonlinear, noninvertible, as well as unknown. This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing. We propose a novel spectral-based estimation procedure and show that we can recover ß* in settings (i.e., classes of link function f) where previous algorithms fail. In general, our algorithm requires only very mild restrictions on the (unknown) functional relationship between yi and 〈xi , ß*〉. We also consider the high dimensional setting where ß* is sparse, and introduce a two-stage nonconvex framework that addresses estimation challenges in high dimensional regimes where p ≫ n. For a broad class of link functions between 〈xi , ß*〉 and yi , we establish minimax lower bounds that demonstrate the optimality of our estimators in both the classical and high dimensional regimes.