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Yuste, R.

Publications and source records attributed to Yuste, R..

11 recordsLinked to original sources

Cortical subnetworks encode context of a visual stimulus

Cortical processing of sensory events is significantly influenced by context. For instance, a repetitive or redundant visual stimulus elicits attenuated cortical responses, but if the same stimulus is unexpected or \"deviant\", responses are augmented. This contextual modulation of sensory processing is likely a fundamental function of neural circuits, yet an understanding of how it is computed is still missing. Using holographic two-photon calcium imaging in awake animals, here we identify three distinct, spatially intermixed ensembles of neurons in mouse primary visual cortex which differentially respond to the same stimulus under separate contexts, including a subnetwork which selectively responds to deviant events. These non-overlapping ensembles are distributed across layers 2-5, though deviance detection is more common in superficial layers. Contextual preferences likely arise locally since they are not present in bottom up inputs from the thalamus or top-down inputs from prefrontal cortex. The functional parcellation of cortical circuits into independent ensembles that encode stimulus context provides a circuit basis underlying cortically based perception of novel or redundant stimuli, a key deficit in many psychiatric disorders.\n\nOne Sentence SummaryVisual cortex represents deviant and redundant stimuli with separate subnetworks.

neuroscience

Triggering visually-guided behavior by holographic activation of pattern completion neurons in cortical ensembles

Neuronal ensembles are building blocks of cortical activity yet it is unclear if they have any causal role in behavior. Here we tested if the precise activation of neuronal ensembles with two-photon holographic optogenetics in mouse primary visual cortex alters behavioral performance in a visual task. Disruption of behaviorally relevant cortical ensembles by activation of non-selective neurons decreased behavioral performance whereas optogenetic targeting of as few as two neurons with pattern completion capability from behaviorally relevant ensembles improved task performance by reliably recalling the whole ensemble. Moreover, in some cases, activation of two pattern completion neurons, in the absence of visual stimulus, triggered correct behavioral responses. Our results demonstrate a causal role of neuronal ensembles in a visually guided behavior and suggest that ensembles could represent perceptual states.

neuroscience

Tracking Activity In A Deformable Nervous System With Motion Correction And Point-Set Registration

The combination of fluorescent probes with time-lapse microscopy allows for the visualization of the entire neuronal activity of small animals, such as worms or cnidarians, over a long period of time. However, large deformations of the animal combined with the natural intermittency of neuronal activity make robust automated tracking of firing fluorescent neurons challenging. Here we present an hybrid approach where (i) a subset of very bright neurons is used as moving reference points (fiducials) to estimate the elastic deformation of the animal; (ii) deformation is frame-by-frame corrected, and firing neurons are aligned at each time with the initial mask; and (iii) point-set registration is used to robustly track the intermittent activity of all the immobilized neurons. We compare different registration strategies with manual tracking performed over {approx}620 neurons over 100 time frames in the cnidarian Hydra vulgaris.\n\nIndex TermsFluorescence imaging, wavelet detection, tracking, point-set registration, elastic deformation, Thin Plate Spline (TPS) transform, Coherent Point Drift (CPD), Hydra.

neuroscience

Reduced repertoire of cortical microstates and neuronal ensembles in medically-induced loss of consciousness

Medically-induced loss of consciousness (mLOC) has been linked to a macroscale break-down of brain connectivity, yet the neural microcircuit correlates of mLOC remain unknown. We applied non-linear t-stochastic neighbor embedding (t-SNE) and Lempel-Ziv-Welch complexity analysis to two-photon calcium imaging and local field potential (LFP) measurements of cortical microcircuit activity across anesthetic depth in mice, and to micro-electrode array recordings in human subjects. We find that mLOC disrupts population activity patterns by i) a reduction of discriminable network microstates and ii) a reduction of independent neuronal ensembles. These alterations are not explained by a simple reduction of neuronal activity and reveal abnormal functional microcircuits. Thus, normal neuronal ensemble dynamics could contribute to the emergence of conscious states.

neuroscience

Seizures start as silent microseizures by neuronal ensembles

Understanding seizure formation and spread remains a critical goal of epilepsy research. While many studies have documented seizure spread, it remains mysterious how they start. We used fast in-vivo two-photon calcium imaging to reconstruct, at cellular resolution, the dynamics of focal cortical seizures as they emerge in epileptic foci (intrafocal), and subsequently propagate (extrafocal). We find that seizures start as intrafocal coactivation of small numbers of neurons (ensembles), which are electrographically silent. These silent \"microseizures\" expand saltatorily until they break into neighboring cortex, where they progress smoothly and first become detectable by LFP. Surprisingly, we find spatially heterogeneous calcium dynamics of local PV interneuron sub-populations, which rules out a simple role of inhibitory neurons during seizures. We propose a two-step model for the circuit mechanisms of focal seizures, where neuronal ensembles first generate a silent microseizure, followed by widespread neural activation in a travelling wave, which is then detected electrophysiologically.

neuroscience

Electrodiffusion model of synaptic potentials in dendritic spines

When modeling electric current flow in neurons and excitable cells, traditional cable theory ignores electrodiffusion (i.e. the interaction between electric fields and ionic diffusion) as it assumes that concentration changes associated with ionic currents are negligible. This assumption, while true for large neuronal compartments, fails when applied to femto-liter size compartments such as dendritic spines - small protrusions that form the main site of synaptic inputs in the brain. Here, we use the Poisson (P) and Nernst-Planck (NP) equations, which relate electric field to charge and couple Ficks law of diffusion to the electric field, to model ion concentration dynamics in dendritic spines. We use experimentally measured voltage transients from spines with nanoelectrodes to explore these dynamics with realistic parameters. We find that (i) passive diffusion and electrodiffusion jointly affect the kinetics of spine excitatory post-synaptic potentials (EPSPs); (ii) spine geometry plays a key role in shaping EPSPs; and, (iii) the spine-neck resistance dynamically decreases during EPSPs, leading to short-term synaptic facilitation. Our formulation can be easily adopted to model ionic biophysics in a variety of nanoscale bio-compartments.

biophysics

Identification and Targeting of Cortical Ensembles

Breaking the neural code requires the characterization of physiological and behavioral correlates of neuronal ensemble activity. To understand how the emergent properties of neuronal ensembles allow an internal representation of the external world, it is necessary to generate empirically grounded models that fully capture ensemble dynamics. We used machine learning techniques, often applied in big data pattern recognition, to identify and target cortical ensembles from mouse primary visual cortex in vivo leveraging recent developments in optical techniques that allowed the simultaneous recording and manipulation of neuronal ensembles with single-cell precision. Conditional random fields (CRFs) allowed us not only to identify cortical ensembles representing visual stimuli, but also to individually target neurons that are functionally key for pattern completion. These results represent the proof-of-principle that machine learning techniques could be used to design close-loop behavioral experiments that involve the precise manipulation of functional cortical ensembles.

neuroscience

Role of inhibitory control in modulating spread of focal ictal activity

Focal seizure propagation is classically thought to be spatially contiguous. However, distribution of seizures through a large-scale epileptic network has been theorized. Here, we used a multielectrode array, wide field calcium imaging, and two-photon calcium imaging to study focal seizure propagation pathways in an acute rodent neocortical 4-aminopyridine model. Although ictal neuronal bursts did not propagate beyond a 2-3 mm region, they were associated with hemisphere-wide field potential fluctuations and parvalbumin-positive interneuron activity outside the seizure focus. While bicuculline surface application enhanced contiguous seizure propagation, focal bicuculline microinjection at sites distant to the 4-aminopyridine focus resulted in epileptic network formation with maximal activity at the two foci. Our study suggests that both classical and epileptic network propagation can arise from localized inhibition defects, and that the network appearance can arise in the context of normal brain structure without requirement for pathological connectivity changes between sites.

neuroscience

Three-Dimensional Two-Photon Optogenetics And Imaging Of Neural Circuits In Vivo

We demonstrate a holographic system for simultaneous three-dimensional (3D) two-photon stimulation and imaging of neural activity in the mouse neocortex in vivo with cellular resolution. Dual two-photon excitation paths are implemented with independent 3D targeting for calcium imaging and precision optogenetics. We validate the usefulness of the microscope by photoactivating local pools of interneurons in awake mice visual cortex in 3D, which suppress the nearby pyramidal neurons response to visual stimuli.

neuroscience

Electro-diffusion modulation of synaptic input in dendritic spines using deconvolved voltage sensor time series

The inward current flowing inside the post-synaptic terminal of a neuron modulates transiently the membrane voltage potential. Most of the excitatory connections are made on dendritic spines characterized by a large variability in their geometry.How the voltage in a spine is modulated by geometry remains elusive due in part to the absence of direct measurements. To understand the spine voltage-current relation, we develop here a model for the voltage and we use it to extract electrical properties from live cell imaging data. We first deconvolve the genetically encoded voltage sensor expressed in hippocampal neurons and then use electro-diffusion theory, to compute the electric field and the ionic flows induced in a dendritic spine.The ionic flow is driven by the electric field coupled to the charge densities that interact through the non-cylindrical spine geometry. We determine the I-V relation and conclude that the spine effective resistance is mostly determined by the neck geometry. This modulation of synaptic inputs by the spine neck is significantly larger than what was expected from traditional cable models. Thus modulating the postsynaptic current can be achieved by changing the number of receptors or by altering the spine geometry which independently affects the transformation of current into voltage.\n\nSignificance statementDendritic spines are geometrical structures receiving most of the excitatory transmission, yet how they modulate voltage from the synaptic current is not clear due to their submicron small size and specific non-cylindrical geometry. We study here the conversion of the synaptic current into voltage modulated by the spine geometry. Our approach is based on the electro-diffusion theory and we show that the spine neck is the main resistance filter, while the voltage is maintained constant in the head. Finally, we extract the effective resistance using a deconvolution of the genetically encoded voltage indicators expressed in hippocampal neurons. The present approach allows studying the electrical properties of many other structures such as glial small protrusions, cilia, and many others.

neuroscience

Multi-scale approaches for high-speed imaging and analysis of large neural populations

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution.\n\nHere we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover neural activity from data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to \"zoom out\" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.\n\nAuthor SummaryThe voxel rate of imaging systems ultimately sets the limit on the speed of data acquisition. These limits often mean that only a small fraction of the activity of large neuronal populations can be observed at high spatio-temporal resolution. For imaging of very large populations with single cell resolution, temporal resolution is typically sacrificed. Here we propose a multi-scale approach to achieve single cell precision using fast imaging at reduced spatial resolution. In the first phase the spatial location and shape of each neuron is obtained at standard spatial resolution; in the second phase imaging is performed at much lower spatial resolution. We show that we can apply a demixing algorithm to accurately recover each neurons activity from the low-resolution data by exploiting the high-resolution cellular maps estimated in the first imaging phase. Thus by decreasing the spatial resolution in the second phase, we can compress the video data significantly, and potentially acquire images over an order-of-magnitude larger area, or image at significantly higher temporal resolution, with minimal loss in accuracy of the recovered neuronal activity. We evaluate this approach on real data from light-sheet and 2-photon calcium imaging.

neuroscience