Kilohertz frame-rate two-photon tomography

Point-scanning two-photon microscopy enables high-resolution imaging within scattering specimens such as the mammalian brain, but sequential acquisition of voxels fundamentally limits its speed. We developed a two-photon imaging technique that scans lines of excitation across a focal plane at multiple angles and computationally recovers high-resolution images, attaining voxel rates of over 1 billion Hz in structured samples. Using a static image as a prior for recording neural activity, we imaged visually evoked and spontaneous glutamate release across hundreds of dendritic spines in mice at depths over 250 µm and frame rates over 1 kHz. Dendritic glutamate transients in anesthetized mice are synchronized within spatially contiguous domains spanning tens of micrometers at frequencies ranging from 1-100 Hz. We demonstrate millisecond-resolved recordings of acetylcholine and voltage indicators, three-dimensional single-particle tracking and imaging in densely labeled cortex. Our method surpasses limits on the speed of raster-scanned imaging imposed by fluorescence lifetime.

Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior

Animals strategically scan the environment to form an accurate perception of their surroundings. Here we investigated the neuronal representations that mediate this behavior. Ca²⁺ imaging and selective optogenetic manipulation during an active sensing task reveals that layer 5 pyramidal neurons in the vibrissae cortex produce a diverse and distributed representation that is required for mice to adapt their whisking motor strategy to changing sensory cues. The optogenetic perturbation degraded single-neuron selectivity and network population encoding through a selective inhibition of active dendritic integration. Together the data indicate that active dendritic integration in pyramidal neurons produces a nonlinearly mixed network representation of joint sensorimotor parameters that is used to transform sensory information into motor commands during adaptive behavior. The prevalence of the layer 5 cortical circuit motif suggests that this is a general circuit computation.

Ring attractor dynamics in the Drosophila central brain

Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal's heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; our study provides physiological evidence of their existence and functional architecture.

Angular velocity integration in a fly heading circuit

Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation.

Robust neuronal dynamics in premotor cortex during motor planning

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

Dynamical feature extraction at the sensory periphery guides chemotaxis

Behavioral strategies employed for chemotaxis have been described across phyla, but the sensorimotor basis of this phenomenon has seldom been studied in naturalistic contexts. Here, we examine how signals experienced during free olfactory behaviors are processed by first-order olfactory sensory neurons (OSNs) of the Drosophila larva. We find that OSNs can act as differentiators that transiently normalize stimulus intensity—a property potentially derived from a combination of integral feedback and feed-forward regulation of olfactory transduction. In olfactory virtual reality experiments, we report that high activity levels of the OSN suppress turning, whereas low activity levels facilitate turning. Using a generalized linear model, we explain how peripheral encoding of olfactory stimuli modulates the probability of switching from a run to a turn. Our work clarifies the link between computations carried out at the sensory periphery and action selection underlying navigation in odor gradients.

DOI:http://dx.doi.org/10.7554/eLife.06694.001

Fruit flies are attracted to the smell of rotting fruit, and use it to guide them to nearby food sources. However, this task is made more challenging by the fact that the distribution of scent or odor molecules in the air is constantly changing. Fruit flies therefore need to cope with, and exploit, this variation if they are to use odors as cues.

Odor molecules bind to receptors on the surface of nerve cells called olfactory sensory neurons, and trigger nerve impulses that travel along these cells. While many studies have investigated how fruit flies can distinguish between different odors, less is known about how animals can use variation in the strength of an odor to guide them towards its source.

Optogenetics is a technique that allows neuroscientists to control the activities of individual nerve cells, simply by shining light on to them. Because fruit fly larvae are almost transparent, optogenetics can be used on freely moving animals. Now, Schulze, Gomez-Marin et al. have used optogenetics in these larvae to trigger patterns of activity in individual olfactory sensory neurons that mimic the activity patterns elicited by real odors. These virtual realities were then used to study, in detail, some of the principles that control the sensory navigation of a larva—as it moves using a series of forward ‘runs’ and direction-changing ‘turns’.

Olfactory sensory neurons responded most strongly whenever light levels changed rapidly in strength (which simulated a rapid change in odor concentration). On the other hand, these neurons showed relatively little response to constant light levels (i.e., constant odors). This indicates that the activity of olfactory sensory neurons typically represents the rate of change in the concentration of an odor. An independent study by Kim et al. found that olfactory sensory neurons in adult fruit flies also respond in a similar way.

Schulze, Gomez-Marin et al. went on to show that the signals processed by a single type of olfactory sensory neuron could be used to predict a larva's behavior. Larvae tended to turn less when their olfactory sensory neurons were highly active. Low levels and inhibition of activity in the olfactory sensory neurons had the opposite effect; this promoted turning. It remains to be determined how this relatively simple control principle is implemented by the neural circuits that connect sensory neurons to the parts of a larva's nervous system that are involved with movement.

DOI:http://dx.doi.org/10.7554/eLife.06694.002

From a meso- to micro-scale connectome: array tomography and mGRASP

Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.

Mapping mammalian synaptic connectivity

Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.

Neuronal circuits underlying persistent representations despite time varying activity

BACKGROUND

Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?

RESULTS

Here we explain how different time-varying representations offered by individual neurons can be woven together to form a coherent, time-invariant, representation. Motivated by two ubiquitous features of the neocortex-redundancy of neural representation and sparse intracortical connections-we derive a network architecture that resolves the apparent contradiction between representation stability and changing neural activity. Unexpectedly, this network architecture exhibits many structural properties that have been measured in cortical sensory areas. In particular, we can account for few-neuron motifs, synapse weight distribution, and the relations between neuronal functional properties and connection probability.

CONCLUSIONS

We show that the intuition regarding network stimulus representation, typically derived from considering single neurons, may be misleading and that time-varying activity of distributed representation in cortical circuits does not necessarily imply that the network explicitly encodes time-varying properties.

A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis

Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.

Effective stimuli for constructing reliable neuron models

The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose.

Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.)--rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

The back photoreaction of the M intermediate in the photocycle of bacteriorhodopsin: mechanism and evidence for two M species

The back photoreaction of the M intermediate in the photocycle of bacteriorhodopsin is investigated both for the native pigment and its D96N mutant. The experimental setup is based on creating the M intermediate by a first pulse, followed by a (blue) laser pulse which drives the back photoreaction of M. Experiments are carried out varying the delay between the two pulses, as well as the temperature over the -25 degrees C-20 degrees C range. It is found that the kinetic patterns of the M back photoreaction change with time after the generation of this intermediate. The data provide independent evidence for the suggestion of a photocycle mechanism based on two distinct M intermediates. They are thus in keeping with the consecutive model of Varo and Lanyi (Biochemistry 30, 5016-5022; 1991), although they cannot exclude other models such as those based on branched or parallel cycles. More generally, we offer a "photochemical" approach to discriminating between intermediate stages in the photocycle which does not depend on spectroscopic and/or kinetic data. While markedly affecting the rate of the M --> N transition in the photocycle, the rate of the thermal step in back photoreaction of M, at both room and low temperatures, is not significantly affected by the D96N mutation. It is proposed that while Asp 96 is the Schiff-base protonating moiety in the M --> N transition, another residue (most probably Asp 85) reprotonates the Schiff base following light absorption by M.

Time-resolved titrations of ASP-85 in bacteriorhodopsin: the multicomponent kinetic mechanism

The Asp-85 residue, located in the vicinity of the retinal chromophore, plays a key role in the function of bacteriorhodopsin (bR) as a light-driven proton pump. In the unphotolyzed pigment the protonation of Asp-85 is responsible for the transition from the purple form (lambdamax = 570 nm) to the blue form (lambdamax = 605 nm) of bR (pKa = 3.5 in 20 mM NaCl). The Purple <=> Blue transition can also be induced by deionization (cation removal). These color changes offer a unique opportunity for time resolving the titration of a protein residue using conventional stopped-flow methodologies. We have studied the Purple <=> Blue equilibration kinetics in bR by exposing the system to pH and to cation jumps. Independently of the equilibration direction (Purple-->Blue or Blue-->Purple) and of the inducing concentration jump ([H+] or [cation]), the kinetics are found to exhibit analogous multicomponent features. Analysis of the data over a range of cation concentrations and pH values leads to the conclusion that the rate-determining step in the overall titration of Asp-85 is proton translocation through a specific proton channel. The multicomponent kinetics, extending over a wide time range (10(-2)-10(4) s), are accounted for in terms of a pH-dependent heterogeneity of proton channels. A model is presented in which the relative weight of four proton channels is determined by the state of protonation of two interacting, channel-controlling, protein residues A1 and A2. These findings bear on the mechanism of the vectorial proton translocation associated with the photocycle of bR.

An optical submicrometer calcium sensor with conductance sensing capability

The identification of chemical species and the measurement of their concentrations with high (submicrometer) spatial resolution are of considerable importance in cell biology. In this article we report the first successful development of a > or = 0.1-micron Ca2+ sensor based on a pulled micropipet, filled with a conducting porous sol-gel glass which was doped with the fluorescent calcium green 1 Ca2+ indicator. Such sensors are potentially capable of measuring Ca2+ concentrations as low as 10(-8) M, in confined volumes, with a three-dimensional resolution which exceeds approximately 0.1 micron. A major advantage of the sensor is its capability to be integrated into a multifunctional probe which will measure chemical analyte concentrations and ion conductance.

Time-resolved titrations of the Schiff base and of the Asp85 residue in artificial bacteriorhodopsins

Deprotonation/protonation processes involving the retinal Schiff base and the Asp85 residue play dominant roles in the light-induced proton pump of bacteriorhodopsin (bR). Although the pKa values of these two moieties in unphotolyzed bR are well established, the kinetics of the respective titrations in the native pigment are difficult to interpret, primarily due to the extreme (nonphysiological) pKa values of the two moieties (12.2 +/- 0.2 and 2.7, in 0.1 M NaCl, for the Schiff base and for Asp85, respectively). These difficulties are circumvented by applying stopped-flow techniques, time resolving the titrations of several artificial bRs in which the pKa values of the above two residues are substantially modified: 13-CF3 bR, pKa (Schiff base) = 8.2 +/- 0.2; 13-demethyl-11,14-epoxy bR, pKa (Schiff base) = 8.2 +/- 0.1 (in 0.1 M NaCl); aromatic bR, pKa (Asp85) = 5.2 +/- 0.1 (in water). The R82Q bR mutant, pKa (Asp85) congruent to 7.2 was also employed. A major objective was to verify whether the basic relationships of homogeneous kinetics obeyed by elementary acid/base systems in solution (primarily, the possibility to express the equilibrium constant as the ratio of the forward and back rate constants) are also obeyed by the Schiff base and Asp85 moieties. We found that this is the case for the Schiff base in the pH range between 7 and 9 but not at lower pH. These observations led to the conclusion that the Schiff base is titrable from the outside medium via a proton channel, which becomes saturated, and thus rate determining, below pH approximately equal to 7.(ABSTRACT TRUNCATED AT 250 WORDS)

A covalent link between the chromophore and the protein backbone of bacteriorhodopsin is not required for forming a photochemically active pigment analogous to the wild type

Bacteriorhodopsin pigments lacking the retinal-Lys-216 covalent bond were prepared by reconstituting the K216G mutant protein with retinal alkylamine Schiff bases. The procedure follows the approach of Zhukovsky et al. [Zhukovsky, E., Robinson, P., & Oprian, D. (1991) Science 251, 558-560] in the case of visual (rhodopsin) pigments. Reconstitution leads to a mixture of three pigments. One of them, bR(K216G)/566a, absorbs (pH = 6.9) at 566 nm. Its absorption is pH-dependent, exhibiting a purple to blue transition. The pigment's laser-induced photocycle patterns are similar to those of wild-type all-trans-bR. A second component, bR(K216G)/566b, exhibits an independent photocycle reminiscent of that of wild-type 13-cis-bR. A third pigment component, bR(K216G)/630, absorbs around 630 nm. Experiments in the presence of a pH dye indicator show that illumination of bR(K216G)/566 produces a detectable proton gradient. It is concluded that a covalent bond between the retinal chromophore and the protein backbone is not a prerequisite for the basic structure and photochemical features of bR or for its proton pump activity.

Thermal equilibration between the M and N intermediates in the photocycle of bacteriorhodopsin

The stages in the photocycle of bacteriorhodopsin (BR) involving the M and N intermediates are investigated using a double pulse excitation method. A first (cycling) pulse at 532 nm is followed, with an appropriate time delay, by a second pulse (337, 406, 446, or 470 nm) which induces the M-->BR back-photoreaction. After depletion by the second pulse a repopulation of M in the millisecond range is observed which is interpreted in terms of a thermal N-->M relaxation. It is thus concluded that a (thermal) M<-->N equilibrium accounts for the biphasic decay of M in the BR photocycle. Other models for this stage of the light-driven proton-pump are therefore unnecessary.

Pharmacokinetic and imaging studies in patients receiving a formulation of liposome-associated adriamycin

Pharmacokinetic and imaging studies in 19 patients receiving liposome-entrapped adriamycin (L-ADM) were carried out within the framework of a Phase I clinical trial (Gabizon et al., 1989a). The formulation of L-ADM tested consisted of 0.2 microM-extruded multilamellar vesicles composed of egg phosphatidylcholine, egg-derived phosphatidyl-glycerol (PG), cholesterol, and ADM intercalated in the fluid lipid bilayer. Plasma clearance of total drug extracted from the plasma after L-ADM infusion followed a biexponential curve with a pattern similar to that reported for free ADM. The plasma concentration of drug circulating in liposome-associated from was also measured in a subgroup of seven patients. Liposome-associated drug was found to be rapidly cleared from plasma. Its ratio to non-liposome-associated drug appeared to correlate with liver reserve, with highest ratios in patients with normal liver function. Liposome clearance, as measured by the plasma concentration of PG in three patients was slower than the clearance of liposome-associated ADM, suggesting that liposomes lose part of their drug payload during circulation. To learn about the liposome organ distribution, imaging studies were carried out with 111Indium-deferoxamine labelled liposomes of the same composition. Liposomes were cleared predominantly by liver and spleen and to a lesser extent by bone marrow in seven out of nine patients. In two patients with active hepatitis and severe liver dysfunction, there was minimal liver uptake and increased spleen and bone marrow uptake. Except for one hepatoma patient, intrahepatic and extrahepatic tumours were not imaged by liposomes, suggesting that liposome uptake is restricted to cells of the reticulo-endothelial system (RES).(ABSTRACT TRUNCATED AT 250 WORDS)

Separation of liposome-associated doxorubicin from non-liposome-associated doxorubicin in human plasma: implications for pharmacokinetic studies

To characterize the pharmacokinetics of liposome-associated drugs, the fraction of drug circulating in liposome-associated form and the absolute plasma drug levels must be determined. In this report, we describe our methodological approach to quantitate plasma liposome-associated doxorubicin separately from protein-bound and free doxorubicin. The method is based on the affinity of a cation-exchange resin for doxorubicin and the repulsion by the same resin of negatively-charged liposomes. The methodology is technically simple and reproducible, and lends itself to the analysis of multiple plasma samples as required in pharmacokinetic studies. The validity of this approach was confirmed by separation of liposome-associated from non-liposome-associated drug using gel exclusion chromatography.