Yale Researchers Make Breakthrough in Possible Depression Treatment

Commonly used as a human anaesthetic and animal tranquilizer, the experimental drug ketamine became famous in the last two decades as a hallucinatory club drug known as “Special K.” Now, researchers at Yale University say the drug is beneficial in treating depression by increasing synaptic connections in parts of the brain that regulate mood and cognition.

Dr. Ronald Duman, who uses Stereo Investigator and Neurolucida at his lab at the Yale School of Medicine was a co-author of the study. He and Dr. George Aghajanian studied rats exposed to stressful situations that produce symptoms similar to those found in human depression.

It appears that depression lowers the number of neuronal synaptic connections in the prefrontal cortex and hippocampus. Current antidepressants reverse these effects, but may take a long time to work, and aren’t successful in all cases. According to Drs. Duman and Aghajanian, ketamine “produces rapid (within hours) antidepressant responses in patients who are resistant to typical antidepressants,” by promoting new synaptic connections and reversing synaptic loss from stress.

“Ketamine works on an entirely different type of neurotransmitter system than current antidepressants, which can take months to improve symptoms of depression and do not work at all for one out of every three patients. Understanding how ketamine works in the brain could lead to the development of an entirely new class of antidepressants, offering relief for tens of millions of people suffering from chronic depression,” according to the Yale School of Medicine press release.

Learn more about the study on NPR.org, and read the free abstract or full paper (by subscription) at ScienceMag.org.

R. S. Duman, G. K. Aghajanian. Synaptic Dysfunction in Depression: Potential Therapeutic Targets. Science, 2012; 338 (6103): 68 DOI: 10.1126/science.1222939


Neurolucida Helps Scientists in Jerusalem Study Synaptic Density in Lactating Mice

A baby cries and her mother’s maternal instincts kick in. She picks her baby up, rocks her, feeds her. Changes in a new mother’s brain compel her to act in ways that ensure her baby’s survival. Researchers at the Hebrew University of Jerusalem are working on learning more about those changes. Their recent focus is on the olfactory bulb – a region of the brain shown to ignite maternal behavior in mice.

“As a scientist and mother I wanted to study plasticity in the maternal brain,” said Hagit Kopel a co-author of the study. “Previous studies showed that olfaction is essential for the production of normal maternal behavior. Therefore, we hypothesized that there are plastic changes in the olfactory system, which accompany the transition into motherhood.”

Continue reading “Neurolucida Helps Scientists in Jerusalem Study Synaptic Density in Lactating Mice” »

Dr. Henry Markram’s Team Uses Neurolucida in New Blue Brain Study

Blue Brain Project researchers have hit an important milestone in their quest to create a virtual model of the human brain. They figured out how to accurately predict the location of synapses in the neocortex; and Neurolucida played an important part.

In a paper published last week in PNAS, the research team led by Dr. Henry Markram at the Brain Mind Institute at the Ecole Polytechnique Fédérale de Lausanne (EPFL), in Lausanne, Switzerland, demonstrated that neurons grow independently of each other, forming connections in places where they accidentally collide. In other words, i is not chemicals that guide axons and dendrites along their path to form synapses.

“Neurons are growing as physically independent of each other as possible. They’re just expressing themselves, saying ‘I want this shape, this is my shape. I’m going to grow like this,’ and when they’ve all grown together, they just take what they get when they bump into each other. It’s just going to grow and rely on accidental collisions to decide where it’s going to form synapses. It’s a remarkable design principle of the brain,” Dr. Markram told EPFL News.

To achieve these results, the researchers used Neurolucida to create 3D models of neurons and form a virtual reconstruction of a cortical microcircuit. They analyzed the places where connections occurred, and found their model to be remarkably similar to the real-brain sample.

Read our previous article about the Blue Brain Project, as well as the research team’s latest paper:

S.L. Hill, Y. Wang, I. Riachi, F. Schürmann, H. Markram: Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits, PNAS, Published online before print September 18, 2012, doi: 10.1073/pnas.1202128109

UVM Scientists Use Neurolucida and Stereo Investigator to Study Neurons in the Avian Iris

During a chicken embryo’s twenty-one days of incubation, its eyes develop in astonishing ways. Muscles form, neurons branch, innervation occurs. Researchers at Dr. Rae Nishi’s lab at the University of Vermont, including two MBF Bioscience staff scientists Julie Simpson, Ph.D. and Julie Keefe, M.S. are studying the development of a chicken embryo’s nervous system. Their specific focus is on the behavior of neurons in the ciliary ganglion – a mass of nerve cells in the eye’s ciliary muscle.

Published last month in Developmental Neurology, their paper “Differential effects of RET and TRKB on axonal branching and survival of parasympathetic neurons” describes the multiple functions of several trophic factors in the development of ciliary ganglion neurons.

According to the paper, the researchers’ principal finding is that the neurotrophic factor receptors RET and TRKB work to ensure the survival of ciliary neurons and foster their axonal outgrowth as they innervate the striated muscle of the avian iris.

To come to this conclusion, the scientists first used Neurolucida to identify specific neurotrophic factors that are important in outgrowth and branching ciliary neurons. Next, they evaluated neuronal survival in the ciliary ganglion, and axonal branching in the iris after blocking neuromuscular transmission and signaling through RET and TRKB. They used Stereo Investigator with the Optical Fractionator probe to perform a design-based stereological count of the ciliary neurons.

“When the normal number of ciliary neurons is decreased by exogenous manipulations such as dTC and dnRET, axonal outgrowth increases to fill synaptic space. However, when neuromuscular transmission is blocked, the lack of activity causes the muscle to attract more axons through retrograde signaling mediated by RET, leading to a higher than normal axonal density,” the researchers said in their paper.

The study, which may be beneficial in neurodegenerative disease research,“suggests that interfering with neuromuscular transmission enhances retrograde signaling between muscle and nerve, which, in turn, promotes axonal branching, endplate formation, and neuronal survival.” (Simpson, Keefe, Nishi, 2012)

“It is always a pleasure to see hard work come to fruition in the form of a publication,” said Dr. Simpson. “I’d like to thank to Dr. Rae Nishi who was a wonderful advisor and mentor during my graduate career at the University of Vermont.”

Simpson, J., Keefe, J. and Nishi, R. (2012), Differential effects of ret and TRKB on axonal branching and survival of parasympathetic neurons. Devel Neurobio. doi: 10.1002/dneu.22036

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{Public domain illustration depicting ciliary muscle via Wikipedia.}

French Scientists Use Neurolucida to Study Plasticity in Adult Neurons

When an adult rat learns new things about its physical environment, the newborn neurons in its brain change – dendrites branch, spines increase, soma grows. But what about mature neurons? Might they also undergo structural changes in response to learning? “Yes,” say scientists at the National Institute of Health and Medical Research and the University of Bordeaux, in Bordeaux, France.

Led by Drs. Valérie Lemaire, Sophie Tronel, and Marie-Françoise Montaron the research team used Neurolucida to analyze the morphology of neurons in the dentate gyrus of the hippocampus, one of the most important regions of the brain for learning and memory. They found that neurons continue to develop into maturity and that these mature neurons play an important role in spatial learning.

Male rats tested in a water maze were the subjects of the study “Long-Lasting Plasticity of Hippocampal Adult-Born Neurons,” published last February in The Journal of Neuroscience. Compared to the control group, the neo-neurons of the experimental group had longer dendrites, increased nodes, increased endings, and a greater cell body area when measured at two-months and four-months after genesis.

To determine if mature cells, already shaped by experience and integrated into the network were still relevant in the spatial learning process, the researchers depleted some of these cells by using Ara-C. The drug, used to treat cancer patients, inhibits cell proliferation. They found that the decreased level of these mature cells resulted in learning delays at the beginning of the water maze training.

“Our results suggest a new perspective with regard to the role of neo-neurons by highlighting that even mature ones can provide an additional source of plasticity to the brain to process memory information,” the authors say in their study.

Read the free abstract here.

Images of adult-born neurons in the dentate gyrus of the hippocampus labeled with a GFP retrovirus (magnification X10 or X20) provided by the authors.

{Lemaire V, Tronel S, Montaron MF, Fabre A, Dugast E, Abrous DN. Long-lasting plasticity of hippocampal adult-born neurons. J Neurosci. 2012 Feb 29;32(9):3101-8}

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Neurolucida Helps Florida Researchers Reconstruct a Region of the Rat Brain

by Dan Peruzzi, PhD

A rat uses its whiskers to get information about its environment. As it scurries along the subway tracks, or burrows into a dumpster, its whiskers send signals to ascending parts of its brain that let it know for example, whether it is safe to jump over that gap or not.

Scientists at the Max Planck Florida Institute are studying the functional responses of neurons in the rat vibrissal cortex. Using a “pipeline” method, developed to use data obtained from animals to recreate parts of the brain “in silico” (1), they have constructed a 3D model of a vibrissal cortical column. The scientists used Neurolucida® to trace neurons so they could be classified according to dendritic morphology and cell body location.

In their paper (2) “Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex,” the scientists classified nine cell types in the barrel cortex, a region of the vibrissal area of the rodent somatosensory cortex. They used these cell-types and parameters such as 3D cell location and quantity, spine and bouton densities, and definitions of pre and post-synaptic partners, to assemble an anatomically realistic network that included synapses at points where boutons and spines overlapped.  Continue reading “Neurolucida Helps Florida Researchers Reconstruct a Region of the Rat Brain” »

In the Forest of the Mind

Using Neurolucida, microscopy, and mice genetically engineered to express a random amount of red, yellow, and blue fluorescent proteins, Okinawa Institute of Science and Technology researcher Hermina Nedelescu has created a fascinating and hypnotic movie of neurons. Nedelescu and colleagues at the Institute’s Computational Neuroscience Unit used Neurolucida and its Virtual Tissue 3D Extension Module and Montaging tools to acquire and stitch together multiple images of Purkinje cells—large neurons  that form elongated branching structures called “dendritic trees”—into a recording showing each tree from different angles and visual locations. As you move around and through the video, the traced cells, highlighted by the “Brainbow” coloring, show the complexity of the structures and location and how the Purkinje cells relate to each other.

Visit the  OIST Computational Neuroscience Unit page for more information on their work.

Movie by Hermina Nedelescu of the OIST Computational Neuroscience Unit (Erik De Schutter, Principal Investigator), in collaboration with Alanna Watt of McGill University, Canada, and Hermann Cuntz of Goethe University, Germany.

This article was edited to add mention of the Montaging tool.

Columbia Scientists Map Neocortical Circuit Connectivity with Neurolucida

A willowy pair of pyramidal cells engage in an intricate dance with a dense mass of basket cells on the cover of the September 14, 2011 issue of the Journal of Neuroscience.

This exquisite image illustrates recent work by Columbia University researchers Dr. Adam M. Packer and Dr. Rafael Yuste, who used Neurolucida to study circuit connectivity in the mammalian neocortex.

According to the paper “Successfully filled and stained neurons were reconstructed using Neurolucida software (MicroBrightField). The neurons were viewed with a 100× oil objective on an Olympus IX71 inverted light microscope or an Olympus BX51 upright light microscope. The Neurolucida program projected the microscope image onto a computer drawing tablet. The neuron’s processes were traced manually while the program recorded the coordinates of the tracing to create a digital, three-dimensional reconstruction. The x- and y-axes formed the horizontal plane of the slice, while the z-axis was the depth. The user defined an initial reference point for each tracing. The z-coordinate was then determined by adjustment of the focus. In addition to the neuron, the pia and white matter were drawn. Axon and dendrite densities were calculated from the Neurolucida reconstruction using the TREES toolbox (Cuntz et al., 2010). The densities were calculated with voxels 5 μm on each side.” (Packer, Yuste, 2011)

Read the open access article in The Journal of Neuroscience.

Packer, Yuste. “Dense, Unspecific Connectivity of Neocortical Parvalbumin-Positive Interneurons: A Canonical Microcircuit for Inhibition?” The Journal of Neuroscience, 14 September 2011, 31(37):13260-13271; doi:10.1523/JNEUROSCI.3131-11.2011

Scientists use Neurolucida Reconstructions to Analyze Dendritic Trees

No two trees are exactly alike, in the forest or in the brain. Though despite the diversity of dendritic arborizations, when it comes to branching out different types of neurons do have a couple things in common, say researchers at the National Institute for Physiological Sciences in Okazaki, Japan.

Led by longtime MBF Bioscience customer Dr. Yoshiyuki Kubota, the research team identified two organizational principles common to the dendritic trees of four different types of neurons.

“First, dendritic cross-sectional areas were found to be proportional to the total lengths of all distal dendritic segments. Second, nonpyramidal neuron dendrites were found to be elliptical, rather than circular, with the degree of ellipticity decreasing with dendritic size and increasing with distance from the soma,” according to the paper published last week in Scientific Reports.

The scientists used Neurolucida to carry out their analysis, forming 3D reconstructions of a Martinotti cell, a fast-spiking basket cell, a double-bouquet cell, and a large basket cell.

“Our data suggest that, in healthy neurons, dendritic structure is more precisely regulated than might be guessed given the diversity of dendritic tree morphologies,” the researchers say in their study. “It will be important for future work to assess the detailed morphology of dendrites in pathological tissue to test if alterations in dendritic tapering and branch point uniformity might participate in generating the cognitive deficits associated with disease.”

Read the full paper “Conserved properties of dendritic trees in four cortical interneuron subtypes” on Scientific Reports.

Yoshiyuki Kubota, Fuyuki Karube, Masaki Nomura, Allan T. Gulledge, Atsushi Mochizuki, Andreas Schertel, Yasuo Kawaguchi. “Conserved properties of dendritic trees in four cortical interneuron subtypes” Scientific Reports, 2011; 1 DOI: 10.1038/srep00089

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Neurolucida Helps Look at Whether Dendrites Can Tell Inputs Apart

What would you do with a neuron if you could activate its synapses in any combination you wanted? Tiago Branco, Beverley A. Clark and Michael Hauser created a chance to do just that (Branco, 2010). The authors, using in-vitro brain slices containing layer II/III pyramidal cells in visual or somatosensory cortex of rats, were able to excite identified spines in any order and with whatever timing. They activated the synapses in one direction and then in the other direction (above is a dendrite not from this study;  “In” is the direction towards the cell body, and “Out” is the direction away from the cell body) to see if the output-signal of the cell would be different.  Along the way they collected more evidence that signal integration happens at the level of the dendrite. The most exciting result is that the output-signals generated in the soma are dependent on the order that the spines were activated.

To study the oblique radial dendrite of the cortical pyramidal cell, one of the smallest dendrites in the brain, multi-site two-photon glutamate uncaging (Judkewitz, 2006; Losonczy, 2006) was used, achieving exquisite control of which spines will be activated when. The idea is to keep the excitatory neurotransmitter, glutamate, hanging around in an inactive form (represented as pink circles above). Photons are used to both convert the glutamate to its active form and to observe the fluorescently-labeled tissue. The amount of glutamate released is believed to only affect one spine, and the time course is such that it can be used to approach physiological conditions. The spines on the dendritic branch can be activated with a spatial and temporal pattern of the authors’ choosing; and the resulting voltage change that can be thought of as the output-signal of the cell, is recorded with an intracellular electrode at the cell body (see the figure below graciously provided by the authors).

Why is the order and timing of synapse activation worth looking at? A neuron functions to collect from the axons of other neurons signals that cause voltage changes in its dendrites; and to pass these signals along to its cell body and axon where the voltage-threshold for an axonal action potential (AP) might or might not be reached. Features of stimuli in sensory pathways can be coded for by the timing of dendritic excitation. One example is in the retina, where individual dendritic branches of retinal starburst amacrine cells show directionally selective signals (Häusser, 2003; Euler, 2002). The authors (Branco, 2010) also point out that temporal and spatial variability in dendritic excitation patterns is especially relevant for circuits with layered input, like the hippocampus, where it could be used by dentate gyrus granule cells to directly detect the sequence of entorhinal cortex activation. Integrative properties of the dendrites appear to be at least one mechanism that can differentially encode spatial and temporal synchrony.

The sensitivity of single dendrites to the order of activation of a defined set of synapses was tested. When activated in isolation, the glutamate excitatory post-synaptic potentials, measured with an intracellular electrode at the cell body, were within physiological range. When the same spines along the dendrite were activated sequentially instead of in isolation, the IN direction always produced a larger somatic voltage response than the OUT direction, and this went along with a bigger chance for an axonal AP. Calcium signals were also larger in the IN than in the OUT direction. The most effective speed to show direction sensitivity was 2.6 microns per second. The dendrite itself can signal the difference between inputs that travel along it in one direction or the other!

What is going on in the dendrites that would cause activation of spines in one direction to give a different output-signal than activation of spines in the other direction? One idea is that dendrites of a neuron see all synapses as equal, and the voltage changes of the membrane caused by the synapses are summed linearly at the axon, possibly resulting in an axonal AP if the threshold is reached. But if they are all equal, and simply summed, the order of activation shouldn’t matter. Another idea is that the dendrites have active conductances, which would result in non-linearities (Häusser, 2003; Losonczy, 2006; Larkum, 2007). Non-linearity means that the whole is different than the sum of its parts; a supralinearity is the situation where the response when the identified synapses are activated sequentially is greater than the sum of the voltage responses from the same synapses activated in isolation. Regenerative events in dendrites are responsible for non-linearities in pyramidal neurons (Schaeffer, 2003); the axonal AP is back-propagating into the dendrites and long-lasting, mainly Ca2+ mediated depolarizations are initiated in the distal regions of apical dendrites. The distal depolarizations are an example of forward propagation (Vetter, 2001). The ability of thin dendritic branches of pyramidal neurons to support forward propagation called a ‘dendritic spike’ has been known for some time. These dendritic spikes are carried by Na+, Ca2+ and predominantly by special glutamate conductances mediated by NMDA receptors (Judkewitz, 2006). In this study, the voltage responses at the cell body were supralinear, meaning if you add together the individual synaptic responses from spines that are activated in isolation, the amplitude is smaller than if the same spines are activated sequentially. Something is boosting the signal to cause the supralinearity. This effect develops gradually with increasing numbers of recruited synapses. When the NMDA Glutamate receptor was blocked, the supralinearity disappeared, and furthermore, direction sensitivity, velocity sensitivity, and detectable dendritic calcium signals were abolished. This evidence points to amplification via NMDA-dependent regenenerative signal boosting and NMDA dendritic spikes.

Where does our program, Neurolucida, come in? The best research uses multiple techniques; and the authors decided to use Neuron, an electrophysiological modeling program, to study the conductances that have been implicated in creating the voltage non-linearities. The neurons were traced using Neurolucida. Neuron has a feature to import Neurolucida tracings. This way the anatomical arrangement of the dendrites is used in the electrophysiological modeling program; the authors could pick one dendritic branch, virtually activate its synapses either in isolation or in sequence, and look at the response at the cell body. Direction sensitivity could be reproduced with a simple model using dendrites with passive electrical properties and synapses containing AMPA and NMDA conductances. The NMDA conductance starts out small and gets bigger over time for the OUT direction and starts out large and gets smaller over time for the IN direction. Direction and velocity sensitivity are abolished by leaving only AMPA receptors and removing the NMDA receptors. There is asymmetric recruitment of NMDA receptors when activating synapses in the different directions. This is due to the smaller input resistance at the tip of the dendrite combined with the highly nonlinear voltage dependence of the NMDA receptor conductance.

So picture it this way. A pyramidal neuron in the sensory cortex is firing axonal APs in response to some sensory stimulus. These APs back propagate into the dendrites. Along with the back propagation the dendrite also experiences forward propagation as a result of active conductances that create a dendritic spike. The back propagation will be maintained or attenuated by the nature of the geometry of the dendritic tree (Schaefer, 2003; Vetter, 2001). Now what if one sensory stimulus sequentially activates the spines along a dendritic branch in the IN direction and another activates it in the OUT direction. For the IN direction, the first synapse activated is at the tip of the dendrite. How is this different than when the first synapse is at the base of the dendrite? First of all, due to differences in location along the geometry of the dendritic tree, the back-propagation voltage signal will be different. Also the dendrites taper, so the tip will have less radius and a greater input resistance than the base. Therefore, the history of what happened to each synapse is different depending on the IN or OUT direction. The NMDA receptors on the dendrites have a non-linear voltage dependence, so the different history or the differences in what just happened to the neighboring synapse, causes a larger signal for the IN than for the OUT direction. The dendrite itself can detect the difference between the two sensory stimuli. The evidence gathered from this work supports the exciting and important conclusion that these cortical neurons use their dendrites to not just pass the signal on, but to change the signal; and furthermore to change the signal based on the time and space pattern of the input to its synapses.

Branco T., Clark B. A., & Häusser M., 2010, Dendritic discrimination of temporal input sequences in cortical        neurons. Science, 329, pp. 1671 – 1675.

Euler T, Detwiler, P.B., & Denk W., 2002, Directionally selective calcium signals in dendrites of Starburst Amacrine Cells. Nature, 418, pp. 845 – 852.

Häusser M. & Mel B., 2003, Dendrites: bug or feature? Current Opinion in Neurobiology, 13, pp. 372 – 383.

Judkewitz B., Roth A., & Häusser M., 2006, Dendritic enlightenment: using patterned two-photon uncaging to reveal the secrets of the brain’s smallest dendrites. Neuron, 50, pp. 180 – 183.

Larkum M.E., Waters J., Sakmann B., & Helmchen, F., 2007, Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons. Journal of Neuroscience, 27, pp. 8999 – 9008.

Losonczy A. & Magee J.C., 2006, Integrative properties of radial oblique dendrites in Hippocampal CA1 Pyramidal Neurons. Neuron, 50, pp. 291 – 307.

Schaefer A.T., Larkum M.E., Sakmann B., & Roth A., 2003, Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. Journal of Neurophysiology, 89, pp. 3143 – 3154.

Vetter P., Roth A., & Häusser M. 2001, Propagation of action potentials in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85, pp. 926 – 937.

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