The field of connectomics would greatly benefit from a genetically-encoded stain for specific proteins that works intracellularly. This could be used to provide positive contrast for axon tracts or to identify if a synapse is excitatory, inhibitory, or neuromodulatory. Horseradish peroxidase (HRP) is a classic approach to EM staining but does not work in reducing environments, such as inside cells. Several groups (Ting, Looger) have tried to engineer away the di-sulfide bonds of HRP, to allow it to work intracellularly, but all have failed to maintain activity after removing these bonds. An alternative approach, miniSOG, from the Tsien lab, is a genetically-encoded tag that can stain via singlet oxygen generation, but requires light to activate it. In Nature Biotechnology, Alice Ting’s group reports APEX, a perioxidase that works intracellularly, which they then engineered to monomerize and improve staining performance. This tool could find extensive use in connectomics of complex brain tissues.
At SfN, a block of three posters by myself, Simon Peron and Daniel O’Connor will showcase three ways to approach the problem of touch coding.
My work on whisker force measurements, and single cell and silicon probe based cortical recordings during active objection localization :
Program#/Poster#:
677.18/KK18
Presentation Title:
Encoding whisking-related variables in the mouse barrel cortex during object localization
Location:
Hall F-J
Presentation time:
Tuesday, Oct 16, 2012, 2:00 PM – 3:00 PM
Authors:
*S. A. HIRES, D. O’CONNOR, D. GUTNISKY, K. SVOBODA;
Janelia Farm Res. Campus, ASHBURN, VA
Simon Peron’s work on recording a complete representation of touch using in-vivo imaging with new G-CaMP variants during a similar behavior :
Program#/Poster#:
677.12/KK12
Presentation Title:
Towards imaging complete representations of whisker touch in the mouse barrel cortex
Location:
Hall F-J
Presentation time:
Tuesday, Oct 16, 2012, 4:00 PM – 5:00 PM
Authors:
*S. P. PERON1, V. IYER2, Z. GUO2, T.-W. CHEN2, D. KIM2, D. HUBER3, K. SVOBODA2;
Daniel O’Connor’s work on constructing synthetic perception of touch and object localization via cortical cell-type specific optogenetic stimulation during behavior :
Program#/Poster#:
677.06/KK6
Presentation Title:
Neural coding for object location revealed using synthetic touch
Location:
Hall F-J
Presentation time:
Tuesday, Oct 16, 2012, 2:00 PM – 3:00 PM
Authors:
*D. H. O’CONNOR1, S. A. HIRES1, Z. GUO1, Q.-Q. SUN2, D. HUBER1, K. SVOBODA1;
This is a must-see session for people interested in touch coding, the whisker system, in-vivo cortical imaging, or synthetic perception via optogenetics.
Three excellent pieces of neuroscience software have been recently updated or freshly released. I have used two of them, Ephus and ScanImage, on a daily basis as primary data collection tools. The third, Neuroptikon, is quite useful for post-hoc illustration of neural circuits.
Ephus is a modular Matlab-based electrophysiology program that can control and record many channels of tools and data simultaneously. Under control of a sophisticated internal looper or external trigger, you can initiate an ephys recording, trigger camera frames, adjust galvo positions, open/close shutters, trigger optical stimulation, punishments, rewards, etc. It is a workhorse program for non-imaging related in vitro and in vivo electrophysiology experiments. Ephus is named for the fabled baseball pitch, and pronounced as “EFF-ess”. As with the pitch, it may trick you at first, but eventually you’re sure to hit a home run. Of course, the name also evokes electrophysiology, which is the fundamental orientation of the project, be it optical or electrical.
Ephus 2.1.0 is a major release, and the only official version at this time. The software is fully described in a publication in Frontiers in Neuroscience. New features include unlimited recording time, with disk streaming, for applications such as EEGs and long traces during in-vivo behavior. A number of additional scripts for in-the-loop control have been added. New configuration/start-up files have been created, with a template to help get up and running quickly. This release also includes a number of bug fixes.
ScanImage is another Matlab-related software program that is used for optical imaging and stimulation of neurons in vitro and in vivo. It finds much use a control platform for 2-photon imaging, glutamate uncaging and laser-scanning photostimulation. An early incarnation is described in this paper by Pologruto, et al. It provides a lot of power right out of the box (bidirectional scanning @ 0.5ms/line, etc) and is easily extensible via custom user function plugins.
Neuroptikon is a sophisticated network visualization tool. It can build Van Essen-like diagrams of any circuit you like, but it is so much more. The direction of communication is animated, and subsets of regions and connections can be brought into focus, which greatly eases the clarity of the network. The diagrams can be built in three-dimensions, to preserve relative topography, or functional grouping. There is simple GUI-based control, while more complex tasks can use a scripting interface. This is great software for anyone who needs to imagine information flow in a complex network.
All three tools are released for free use under the HHMI/Janelia Farm open source license.
Wired has a nice lay-person write-up of the rapidly developing field of compressed sensing. This is a technique that allows accurate reconstructions of highly undersampled sparse datasets. This field really took off in 2004 when Emmanuel J. Candès discovered that a tomography phantom image could be reconstructed exactly even with data deemed insufficient by the Nyquist-Shannon criterion. It is probably the hottest topic in imaging theory today.
Modified Shepp-Logan phantom with enhanced contrast for visual perception.
According to this review, Compressed Sensing MRI, its successful application requires three conditions to be met :
Transform Sparsity: The desired image must have a sparse representation in a known transform domain (i.e., it must be compressible by transform coding),
Incoherence of Undersampling Artifacts: The aliasing artifacts in a linear reconstruction caused by k-space undersampling must be incoherent (noise-like) in the sparsifying transform domain.
Nonlinear Reconstruction: The image must be reconstructed by a non-linear method which enforces both sparsity of the image representation and consistency of the reconstruction with the acquired samples.
These conditions are well met by MRI imaging. This decoding technique dramatically shortens the required sampling times in an MRI magnet, which reduces the impact of motion artifacts, the bane of high-resolution MRI.
Unfortunately, I don’t think it is very applicable to situations where signal/noise of the underlying source is poor, like counting action potentials in shot-noise limited in vivo calcium imaging. But it’s use is spreading into other related problems, such as mapping the functional connectivity of neural circuitry. Tao Hu and Mitya Chklovvskii apply the compressed sensing algorithms in Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME) from the latest Advances in Neural Information Processing Systems journal. They measure a post-synaptic neuron’s voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. The sparseness of connectivity allows them to map connectivity much faster than by a sequential method.
Three papers are out online in Nature Methods that show big improvements in calcium imaging with genetically encoded sensors. They are are based on the fluorescence intensity indicator, GCaMP. GCaMP, first developed by Junichi Nakai, consists of a GFP that has been circularly permuted so that the N and C termini are fused and new termini are made in the middle of the protein. Fused to one terminus is calmodulin and the other is a peptide, M13, that calmodulin (CaM) binds to in the presence of calcium. The name is supposed to look like GFP with a CaM inserted into it, G-CaM-P. Normally the GFP is dim, as there is a hole from the outside of its barrel into the chromophore. Upon binding calcium, this hole is plugged and fluorescence increases.
The first paper, A genetically encoded reporter of synaptic activity in vivo, from Leon Lagnado’s group, targets GCaMP2 to the outer surface of synaptic vesicles. This localization allows the fluorescence signal to be confined to the presynaptic terminal, where calcium fluxes in response to action potentials are high. This targeting improves the response magnitude of GCaMP2 and permits the optical recording of synaptic inputs into whatever region of the brain one looks at. They demonstrate the technique in live zebrafish.
In the second paper, Optical interrogation of neural circuits in Caenorhabditis elegans, from Sharad Ramanathan’s group, GCaMP2 has been combined with Channelrhodopsin-2 to perform functional circuit mapping in the worm. Since the worm’s structural wiring diagram has been essentially solved, functional data could say much about how “thick” the wires between each cell are. Unfortunately, with GCaMP2, the responses are too slow and weak to distinguish direct from indirect connections.
Finally, we have published a paper, Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators, describing the improved GCaMP3. This indicator has between 2-10x better signal to noise than GCaMP2, D3cpv and TN-XXL, depending on the system you are using. It’s kinetics are faster and it is more photostable than FRET indicators, and the responses are huge. When expressed in motor cortex of the mouse, neuronal activity is easily seen directly in the raw data. Furthermore, the sensor can be expressed stably for months, making it a potential tool for observing how learning reshapes the patterns of activity in the cortex.
Imaging of mouse motor cortex (M1) expressing the genetically-encoded calcium indicator GCaMP3 through a cortical window. After 72 days of GCaMP3 expression, large fluorescence transients can be seen in many neurons that are highly correlated with mouse running.
GCaMP3 is not perfect. It cannot reliably detect single action potential in vivo in mammals, though I doubt that any existing GECI can. Work continues on future generations of GCaMP that may achieve 100% fidelity in optical reading of the bits in the brain. However, there is considerable evidence from a number of groups that have been beta-testing the sensor, including the Tank lab of “quake mouse” fame, that it is a significant leap forward and unlocks much of the fantastic and fantasized potential of genetically-encoded calcium indicators.
Comparison of fluorescence changes in response to trains of action potentials in acute cortical slices.
I will try to post a more complete writeup of GCaMP3 for Brain Windows soon, with an unbiased eye to its strengths and weaknesses. We worked very hard to carefully characterize this sensor’s effects on cellular and circuit properties. If you have any questions about GCaMP3, please post them to the comments.
Annual Reviews of Neuroscience published their 2009 issue recently. These articles are usually a great way to catch up with a field, particularly when they are recently published. Here are a few that might be of interest to the Brain Windows reader.
Sensory experience and learning alter sensory representations in cerebral cortex. The synaptic mechanisms underlying sensory cortical plasticity have long been sought. Recent work indicates that long-term cortical plasticity is a complex, multicomponent process involving multiple synaptic and cellular mechanisms. Sensory use, disuse, and training drive long-term potentiation and depression (LTP and LTD), homeostatic synaptic plasticity and plasticity of intrinsic excitability, and structural changes including formation, removal, and morphological remodeling of cortical synapses and dendritic spines. Both excitatory and inhibitory circuits are strongly regulated by experience. This review summarizes these findings and proposes that these mechanisms map onto specific functional components of plasticity, which occur in common across the primary somatosensory, visual, and auditory cortices.
Diffusion imaging can be used to estimate the routes taken by fiber pathways connecting different regions of the living brain. This approach has already supplied novel insights into in vivo human brain anatomy. For example, by detecting where connection patterns change, one can define anatomical borders between cortical regions or subcortical nuclei in the living human brain for the first time. Because diffusion tractography is a relatively new technique, however, it is important to assess its validity critically. We discuss the degree to which diffusion tractography meets the requirements of a technique to assess structural connectivity and how its results compare to those from the gold-standard tract tracing methods in nonhuman animals. We conclude that although tractography offers novel opportunities it also raises significant challenges to be addressed by further validation studies to define precisely the limitations and scope of this exciting new technique.
The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.
Brian A. Wilt, Laurie D. Burns, Eric Tatt Wei Ho, Kunal K. Ghosh, Eran A. Mukamel, and Mark J. Schnitzer
Since the work of Golgi and Cajal, light microscopy has remained a key tool for neuroscientists to observe cellular properties. Ongoing advances have enabled new experimental capabilities using light to inspect the nervous system across multiple spatial scales, including ultrastructural scales finer than the optical diffraction limit. Other progress permits functional imaging at faster speeds, at greater depths in brain tissue, and over larger tissue volumes than previously possible. Portable, miniaturized fluorescence microscopes now allow brain imaging in freely behaving mice. Complementary progress on animal preparations has enabled imaging in head-restrained behaving animals, as well as time-lapse microscopy studies in the brains of live subjects. Mouse genetic approaches permit mosaic and inducible fluorescence-labeling strategies, whereas intrinsic contrast mechanisms allow in vivo imaging of animals and humans without use of exogenous markers. This review surveys such advances and highlights emerging capabilities of particular interest to neuroscientists.
Who were the primary developers of two-photon microscopy for visualizing brain activity? Following Watt Webb’sseminal work, it was Winfried Denk, David Tank, Karel Svoboda, and David Kleinfeld. What do these four have in common? They all worked at Bell Labs, and they all do imaging in rodent somatosensory cortex. Primary somatosensory cortex (SI), particularly barrel cortex has many advantages. You can directly observe the input (whisker touching), you can get behavioral output, the cortex is smooth, has a vivid characteristic pattern of cytochrome oxidase staining and is accessible to a cortical window. Consequently, SI is one of the best characterized regions of cortex. Far less understood is the structure and function of secondary somatosensory cortex (SII), but it likely plays an essential role in rodent sensory perception. For Journal Club #3, I review what is know of the location, structure, connectivity, physiology and function of SII.
Just saw a cool informal talk from Andreas Burkhalter about the mouse visual cortex.He has a fascinating paper, Area Map of Mouse Visual Cortex, in the Journal of Comparative Neurology, in which he identifies not just three or four areas of mouse visual cortex, but twelve! Each area has a complete map of the entire visual field.He combines triplet injections of Di-I, Di-O and BDA as fiducial markers with a label for callosal connections. He fixes the tissue in a manner that allows the unrolling and flattening of the entire mouse cortex. This allows him to segment and show the orientation of each field in a single cortical layer in the same slice. Different layers give different patterns of projection. Given the richness of the data obtained, I’m surprised that more systems neuroscientists don’t use identical techniques.
Triple labeling of mouse visual cortex
He has also created a wiring diagram of each of these and shown that receptive field size increases with the depth in the visual system hierarchy. He also noted that although Michael Stryker finds 50% of visual cortex neurons are direction selection (when stimulated by drifting gratings), he finds only 10% are direction selective when using random dot patterns. Presumably, drifting gratings provide additional cues beyond direction of motion that confound analysis. For such a ‘blind’ creature, mice sure have a complex pattern of circuitry to process visual information.
Jeff Lichtman‘s Brainbow mouse paper is out! Not that I really need to report that news, as it is, of course, on the cover of Nature. Jean Livet comes up with some really clever genetic strategies involving incompatible, overlapping Lox sites to generate random, combinatorial patterns of multiple fluorescent proteins inside the cell. Around 90 different shades can be discerned by spectral deconvolution.
Besides making pretty covers, why is this so cool?
Well, this technique provides a method for generating high resolution maps of the brain. With a single fluorescent tag, the processes of neighboring cells blur together and became impossible to trace unambiguously. With brainbow, many neighboring axons are clearly resolvable. This is the perfect genetic tool to use for a large-scale, all-out effort for the complete mapping of the circuitry of the mouse brain. It would be a tremendous challenge, but perhaps no more difficult than the human genome project. A large public consortium, or a Celera of the brain can really attack the connectivity problem now.
Of course, there still is the more difficult problem of showing the functional connectivity of the circuit map. Then again, this technique isn’t limited to swapping in static fluorescent tags. The insert cassette could be doped with a single FP functional indicator like G-CaMP2… Would this allow the combination of static circuit mapping with functional testing?
Brain Windows 
Recent Comments