Sensitive red protein calcium indicators for imaging neural activity

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Genetically encoded calcium indicators (GECIs) allow measurement of activity in large populations of neurons and in small neuronal compartments, over times of milliseconds to months. Although GFP-based GECIs are widely used for in vivo neurophysiology, GECIs with red-shifted excitation and emission spectra have advantages for in vivo imaging because of reduced scattering and absorption in tissue, and a consequent reduction in phototoxicity. However, current red GECIs are inferior to the state-of-the-art GFP-based GCaMP6 indicators for detecting and quantifying neural activity. Here we present improved red GECIs based on mRuby (jRCaMP1a, b) and mApple (jRGECO1a), with sensitivity comparable to GCaMP6. We characterized the performance of the new red GECIs in cultured neurons and in mouse, Drosophila, zebrafish and C. elegans in vivo. Red GECIs facilitate deep-tissue imaging, dual-color imaging together with GFP-based reporters, and the use of optogenetics in combination with calcium imaging.

A probability distribution over latent causes in the orbitofrontal cortex

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The orbitofrontal cortex (OFC) has been implicated in both the representation of “state”, in studies of reinforcement learning and decision making, and also in the representation of “schemas”, in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or “latent cause” that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes.

Conformational dynamics of Tau in the cell quantified by an intramolecular FRET biosensor in physiological and pathological context

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Impaired interactions of Tau protein with microtubules (MT) and Tau misfolding play a key role in Alzheimer disease (AD) and other neurodegenerative diseases collectively named Tauopathies. However, little is known about the molecular conformational changes that underlie Tau misfolding and aggregation in pathological conditions, due to the difficulty of studying structural aspects of this intrinsically unfolded protein, particularly in the context of living cells. Here we developed a new Conformational-Sensitive Tau sensor (CST), based on human Tau full length protein, to investigate the changes in 3D conformation and aggregation state of Tau upon modulation of its interactions with MTs in living cells, in physiological and pathological conditions. After showing that the CST fully preserves functional Tau activities in living cells, we demonstrated that MT-bound Tau displays a loop-like conformation, while soluble Tau assumes a relaxed conformation. The imaging readout based on CST allowed to discover new conformational properties of full length Tau in living cells, when challenged with Alzheimer-relevant seeds from different sources, and to learn about different ways to induce the self-aggregation of full length Tau in cells. Furthermore, it allowed to investigate the contribution to the pathology of point mutations known to alter Tau/MTs interaction.

A Dendritic Disinhibitory Circuit Mechanism for Pathway-Specific Gating

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In this work we propose that a disinhibitory circuit motif, which recently gained experimental support, can instantiate flexible routing of information flow along selective pathways in a complex system of cortical areas according to behavioral demands (pathway-specific gating). We developed a network model of pyramidal neurons and three classes of interneurons, with connection probabilities constrained by data. If distinct input pathways cluster on separate dendritic branches of pyramidal neurons, then a pathway can be gated-on by disinhibiting targeted dendrites. We show that this branch-specific disinhibition can be achieved despite dense interneuronal connectivity, even under the assumption of random connections. We found clustering of input pathways on dendrites can emerge through synaptic plasticity regulated by disinhibition. This gating mechanism in a neural circuit is further demonstrated by performing a context-dependent decision-making task. Our findings suggest a microcircuit architecture that harnesses dendritic computation and diverse inhibitory neuron types to subserve cognitive flexibility.

MECP2 regulates cortical plasticity underlying a learned behavior in adult female mice

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Functional Genetic Screen to Identify Interneurons Governing Behaviorally Distinct Aspects of Drosophila Larval Motor Programs


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Drosophila larval crawling is an attractive system to study patterned motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors including head casts, turning, and feeding. It is likely that some neurons are used in all these behaviors (e.g. motor neurons), but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We used 165 Janelia CRM-Gal4 lines chosen for sparse neuronal expression to express the warmth-inducible neuronal activator TrpA1 and screened for locomotor defects. The primary screen measured forward locomotion velocity, and we identified 63 lines that had locomotion velocities significantly slower than controls following TrpA1 activation (28oC). A secondary screen was performed on these lines, revealing multiple discrete behavioral phenotypes including slow forward locomotion, excessive reverse locomotion, excessive turning, excessive feeding, immobile, rigid paralysis, and delayed paralysis. While many of the Gal4 lines had motor, sensory, or muscle expression that may account for some or all of the phenotype, some lines showed specific expression in a sparse pattern of interneurons. Our results show that distinct motor programs utilize distinct subsets of interneurons, and provide an entry point for characterizing interneurons governing different elements of the larval motor program.

Learning In Spike Trains: Estimating Within-Session Changes In Firing Rate Using Weighted Interpolation


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The electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.