The Mind of a Mouse.

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain.

Dense reconstruction of synaptic connectivity requires high-resolution electron microscopy images of entire brains and tools to efficiently trace neuronal wires across the volume. To generate such a resource, we sectioned and imaged a larval zebrafish brain by serial block-face electron microscopy at a voxel size of 14 × 14 × 25 nm3. We segmented the resulting dataset with the flood-filling network algorithm, automated the detection of chemical synapses and validated the results by comparisons to transmission electron microscopic images and light-microscopic reconstructions. Neurons and their connections are stored in the form of a queryable and expandable digital address book. We reconstructed a network of 208 neurons involved in visual motion processing, most of them located in the pretectum, which had been functionally characterized in the same specimen by two-photon calcium imaging. Moreover, we mapped all 407 presynaptic and postsynaptic partners of two superficial interneurons in the tectum. The resource developed here serves as a foundation for synaptic-resolution circuit analyses in the zebrafish nervous system.

High-precision automated reconstruction of neurons with flood-filling networks.

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.

Space-time wiring specificity supports direction selectivity in the retina.

How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of 'citizen neuroscientists'. On the basis of quantitative analyses of contact area and branch depth in the retina, we find evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, whereas another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such 'space-time wiring specificity' could endow SAC dendrites with receptive fields that are oriented in space-time and therefore respond selectively to stimuli that move in the outward direction from the soma.

Connectomic reconstruction of the inner plexiform layer in the mouse retina.

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer--the main computational neuropil region in the mammalian retina--the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.

High-resolution whole-brain staining for electron microscopic circuit reconstruction.

Currently only electron microscopy provides the resolution necessary to reconstruct neuronal circuits completely and with single-synapse resolution. Because almost all behaviors rely on neural computations widely distributed throughout the brain, a reconstruction of brain-wide circuits-and, ultimately, the entire brain-is highly desirable. However, these reconstructions require the undivided brain to be prepared for electron microscopic observation. Here we describe a preparation, BROPA (brain-wide reduced-osmium staining with pyrogallol-mediated amplification), that results in the preservation and staining of ultrastructural details throughout the brain at a resolution necessary for tracing neuronal processes and identifying synaptic contacts between them. Using serial block-face electron microscopy (SBEM), we tested human annotator ability to follow neural 'wires' reliably and over long distances as well as the ability to detect synaptic contacts. Our results suggest that the BROPA method can produce a preparation suitable for the reconstruction of neural circuits spanning an entire mouse brain.

Structural neurobiology: missing link to a mechanistic understanding of neural computation.

High-resolution, comprehensive structural information is often the final arbiter between competing mechanistic models of biological processes, and can serve as inspiration for new hypotheses. In molecular biology, definitive structural data at atomic resolution are available for many macromolecules; however, information about the structure of the brain is much less complete, both in scope and resolution. Several technical developments over the past decade, such as serial block-face electron microscopy and trans-synaptic viral tracing, have made the structural biology of neural circuits conceivable: we may be able to obtain the structural information needed to reconstruct the network of cellular connections for large parts of, or even an entire, mouse brain within a decade or so. Given that the brain's algorithms are ultimately encoded by this network, knowing where all of these connections are should, at the very least, provide the data needed to distinguish between models of neural computation.

Wiring specificity in the direction-selectivity circuit of the retina.

The proper connectivity between neurons is essential for the implementation of the algorithms used in neural computations, such as the detection of directed motion by the retina. The analysis of neuronal connectivity is possible with electron microscopy, but technological limitations have impeded the acquisition of high-resolution data on a large enough scale. Here we show, using serial block-face electron microscopy and two-photon calcium imaging, that the dendrites of mouse starburst amacrine cells make highly specific synapses with direction-selective ganglion cells depending on the ganglion cell's preferred direction. Our findings indicate that a structural (wiring) asymmetry contributes to the computation of direction selectivity. The nature of this asymmetry supports some models of direction selectivity and rules out others. It also puts constraints on the developmental mechanisms behind the formation of synaptic connections. Our study demonstrates how otherwise intractable neurobiological questions can be addressed by combining functional imaging with the analysis of neuronal connectivity using large-scale electron microscopy.

Staining and embedding the whole mouse brain for electron microscopy.

The development of methods for imaging large contiguous volumes with the electron microscope could allow the complete mapping of a whole mouse brain at the single-axon level. We developed a method based on prolonged immersion that enables staining and embedding of the entire mouse brain with uniform myelin staining and a moderate preservation of the tissue's ultrastructure. We tested the ability to follow myelinated axons using serial block-face electron microscopy.

Imaging in vivo: watching the brain in action.

The appeal of in vivo cellular imaging to any neuroscientist is not hard to understand: it is almost impossible to isolate individual neurons while keeping them and their complex interactions with surrounding tissue intact. These interactions lead to the complex network dynamics that underlie neural computation which, in turn, forms the basis of cognition, perception and consciousness. In vivo imaging allows the study of both form and function in reasonably intact preparations, often with subcellular spatial resolution, a time resolution of milliseconds and a purview of months. Recently, the limits of what can be achieved in vivo have been pushed into terrain that was previously only accessible in vitro, due to advances in both physical-imaging technology and the design of molecular contrast agents.

Low-dosage Maximum-A-Posteriori Focusing and Stigmation.

Radiation damage is often an issue during high-resolution imaging, making low-dose focusing and stigmation essential, in particular when no part of the sample can be "sacrificed" for this. An example is serial block-face electron microscopy, where the imaging resolution must be kept optimal during automated acquisition that can last months. Here, we present an algorithm, which we call "Maximum-A-Posteriori Focusing and Stigmation (MAPFoSt)," that was designed to make optimal use of the available signal. We show that MAPFoSt outperforms the built-in focusing algorithm of a commercial scanning electron microscope even at a tenfold reduced total dose. MAPFoSt estimates multiple aberration modes (focus and the two astigmatism coefficients) using just two test images taken at different focus settings. Using an incident electron dose density of 2,500 electrons/pixel and a signal-to-noise ratio of about one, all three coefficients could be estimated to within <7% of the depth of focus, using 19 detected secondary electrons per pixel. A generalization to higher-order aberrations and to other forms of imaging in both two and three dimensions appears possible.

Large-scale automated histology in the pursuit of connectomes.

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain's computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

Visually evoked activity in cortical cells imaged in freely moving animals.

We describe a miniaturized head-mounted multiphoton microscope and its use for recording Ca(2+) transients from the somata of layer 2/3 neurons in the visual cortex of awake, freely moving rats. Images contained up to 20 neurons and were stable enough to record continuously for >5 min per trial and 20 trials per imaging session, even as the animal was running at velocities of up to 0.6 m/s. Neuronal Ca(2+) transients were readily detected, and responses to various static visual stimuli were observed during free movement on a running track. Neuronal activity was sparse and increased when the animal swept its gaze across a visual stimulus. Neurons showing preferential activation by specific stimuli were observed in freely moving animals. These results demonstrate that the multiphoton fiberscope is suitable for functional imaging in awake and freely moving animals.

Second-harmonic generation imaging of membrane potential with retinal analogues.

Second-harmonic generation (SHG) by membrane-incorporated probes is a nonlinear optical signal that is voltage-sensitive and the basis of a sensitive method for imaging membrane potential. The voltage dependence of SHG by four different probes, three retinoids (all-trans retinal), and two new retinal analogs, 3-methyl-7-(4'-dimethylamino-phenyl)-2,4,6-heptatrienal (AR-3) and 3,7-dimethyl-9-(4'-dimethylamino-phenyl)-2,4,6,8-nonatetraenal (AR-4), and a styryl dye (FM4-64), were compared in HEK-293 cells. Results were analyzed by fitting data with an expression based on an electrooptic mechanism for SHG, which depends on the complex-valued first- and second-order nonlinear electric susceptibilities (χ² and χ³) of the probe. This gave values for the voltage sensitivity at the cell's resting potential, the voltage where the SHG is minimal, and the amplitude of the signal at that voltage for each of the four compounds. These measures show that χ² and χ³ are complex numbers for all compounds except all-trans retinal, consistent with the proximities of excitation and/or emission wavelengths to molecular resonances. Estimates of probe orientation and location in the membrane electric field show that, for the far-from-resonance case, the shot noise-limited signal/noise ratio depends on the location of the probe in the membrane, and on χ³ but not on χ².

Targeted patch-clamp recordings and single-cell electroporation of unlabeled neurons in vivo.

Here we describe an approach for making targeted patch-clamp recordings from single neurons in vivo, visualized by two-photon microscopy. A patch electrode is used to perfuse the extracellular space surrounding the neuron of interest with a fluorescent dye, thus enabling the neuron to be visualized as a negative image ('shadow') and identified on the basis of its somatodendritic structure. The same electrode is then placed on the neuron under visual control to allow formation of a gigaseal ('shadowpatching'). We demonstrate the reliability and versatility of shadowpatching by performing whole-cell recordings from visually identified neurons in the neocortex and cerebellum of rat and mouse. We also show that the method can be used for targeted in vivo single-cell electroporation of plasmid DNA into identified cell types, leading to stable transgene expression. This approach facilitates the recording, labeling and genetic manipulation of single neurons in the intact native mammalian brain without the need to pre-label neuronal populations.

Axons and synaptic boutons are highly dynamic in adult visual cortex.

While recent studies of synaptic stability in adult cerebral cortex have focused on dendrites, how much axons change is unknown. We have used advances in axon labeling by viruses and in vivo two-photon microscopy to investigate axon branching and bouton dynamics in primary visual cortex (V1) of adult Macaque monkeys. A nonreplicative adeno-associated virus bearing the gene for enhanced green fluorescent protein (AAV.EGFP) provided persistent labeling of axons, and a custom-designed two-photon microscope enabled repeated imaging of the intact brain over several weeks. We found that large-scale branching patterns were stable but that a subset of small branches associated with terminaux boutons, as well as a subset of en passant boutons, appeared and disappeared every week. Bouton losses and gains were both approximately 7% of the total population per week, with no net change in the overall density. These results suggest ongoing processes of synaptogenesis and elimination in adult V1.

High speed optical coherence microscopy with autofocus adjustment and a miniaturized endoscopic imaging probe.

Optical coherence microscopy (OCM) is a promising technique for high resolution cellular imaging in human tissues. An OCM system for high-speed en face cellular resolution imaging was developed at 1060 nm wavelength at frame rates up to 5 Hz with resolutions of < 4 microm axial and < 2 microm transverse. The system utilized a novel polarization compensation method to combat wavelength dependent source polarization and achieve broadband electro-optic phase modulation compatible with ultrahigh axial resolution. In addition, the system incorporated an auto-focusing feature that enables precise, near real-time alignment of the confocal and coherence gates in tissue, allowing user-friendly optimization of image quality during the imaging procedure. Ex vivo cellular images of human esophagus, colon, and cervix as well as in vivo results from human skin are presented. Finally, the system design is demonstrated with a miniaturized piezoelectric fiber-scanning probe which can be adapted for laparoscopic and endoscopic imaging applications.

Convolutional networks can learn to generate affinity graphs for image segmentation.

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

A dendrite-autonomous mechanism for direction selectivity in retinal starburst amacrine cells.

Detection of image motion direction begins in the retina, with starburst amacrine cells (SACs) playing a major role. SACs generate larger dendritic Ca(2+) signals when motion is from their somata towards their dendritic tips than for motion in the opposite direction. To study the mechanisms underlying the computation of direction selectivity (DS) in SAC dendrites, electrical responses to expanding and contracting circular wave visual stimuli were measured via somatic whole-cell recordings and quantified using Fourier analysis. Fundamental and, especially, harmonic frequency components were larger for expanding stimuli. This DS persists in the presence of GABA and glycine receptor antagonists, suggesting that inhibitory network interactions are not essential. The presence of harmonics indicates nonlinearity, which, as the relationship between harmonic amplitudes and holding potential indicates, is likely due to the activation of voltage-gated channels. [Ca(2+)] changes in SAC dendrites evoked by voltage steps and monitored by two-photon microscopy suggest that the distal dendrite is tonically depolarized relative to the soma, due in part to resting currents mediated by tonic glutamatergic synaptic input, and that high-voltage-activated Ca(2+) channels are active at rest. Supported by compartmental modeling, we conclude that dendritic DS in SACs can be computed by the dendrites themselves, relying on voltage-gated channels and a dendritic voltage gradient, which provides the spatial asymmetry necessary for direction discrimination.