NeuronStudio

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NeuronStudio was a non-commercial program created at Icahn School of Medicine at Mount Sinai by the Computational Neurobiology and Imaging Center. This program performed automatic tracing and reconstruction of neuron structures from confocal image stacks. The resulting models were then exported to a file using standard formats for further processing, modeling, or for statistical analyses. NeuronStudio handled morphologic details on scales spanning local Dendritic spine geometry through complex tree topology to the gross spatial arrangement of multi-neuron networks. Its capability for automated digitization avoided the subjective errors inherent in manual tracing. The program ceased to be supported in 2012 and the project pages were eventually removed from the ISMMS Website. Its documentation and the Windows source code however are still available via the Internet Archive.

Contents

The NeuronStudio graphical user interface (Windows version). Neuronstudio3.jpg
The NeuronStudio graphical user interface (Windows version).

Deconvolution

Deconvolution of imaged data is essential for an accurate 3D reconstructions. Deconvolution is an image restoration approach where 'a priori' knowledge of the optical system in the form of a point spread function (PSF) is used to obtain a better estimate of the object. A point spread function can be either calculated from the actual microscope parameters, measured with beads, or estimated and iteratively refined (Blind deconvolution). PSFs can be adjusted locally to account for variations in refractive characteristics of the tissue with depth and sample characteristics. For automated use with large, tiled tissue blocks, this is faster and more accurate than using an experimentally determined PSF.

Skeletonization and diameter estimation

Quantization errors arise in standard skeletonization algorithms from the integer nature of digital images. The requirement for accurate representation of fine dendritic geometry has required the development of novel adaptations of standard skeletonization and diameter estimation algorithms to correct for these quantization errors. Iterative thinning skeletonization methods can provide a distance in voxels from each tree node to the surface of the object. This distance is the D6 metric, obtained by counting the number of voxels as they are removed in the minimal 6-connected path from the surface to the medial axis. In existing skeletonization or vectorization algorithms for dendritic morphometry, the branch cross-section at any node is approximated as circular, with the D6 metric providing the single diameter estimate. The precision of this diameter estimate is limited to the physical size of the voxels. For small structures such as thin dendrites and spines, comprising only a few voxels even at maximal imaging resolution, the error can be significant if this measure is used directly (see figure). To minimize quantization error and evaluate more precisely the geometry of the nodes, a new estimation technique exists, the Rayburst Sampling Algorithm that uses the original grayscale data rather than the segmented images for precise, continuous radius estimation, and multidirectional radius sampling to more accurately represent non-circular branch cross-sections and non-spherical spine heads. [1] [2]

Rayburst algorithm

3D Representation of the Rayburst sampling core. Rayburst.jpg
3D Representation of the Rayburst sampling core.

The Rayburst Sampling Algorithm uses the original grayscale data rather than the segmented images for precise, continuous radius estimation, and multidirectional radius sampling to more accurately represent non-circular branch cross-sections and non-spherical spine heads. The algorithm precomputes an array of unit vectors which sample the data in multiple directions, (the Sampling Core) from which an estimate of the node's geometry is computed. Accurate representation of each direction by the sampling core requires that the N vectors should be uniformly spaced over the unit sphere. The algorithm uses a particle physics simulation in which a set N of randomly oriented unit vectors is generated, resulting in a random, nonuniform distribution of points on the sphere. Each particle then receives a repulsive force from every other particle, proportional to the inverse square of the distance between them. By iteratively displacing the particle in the direction of the resultant forces, the particles rearrange themselves. This system will tend to a stable, minimum energy configuration within approximately 40 iterations, where each particle is maximally separated from its closest neighbors. The information can be used to reconstruct 3D branches of arbitrarily irregular shapes. The diameter of an equivalent circular cross-section is computed in the plane normal to the medial axis using the Median Lower Band Diameter (MLBD). To compute the MLBD, take the set of samples and add the corresponding pairs of opposite vectors. Sort the vectors by size, define the lower band as the lower 50%, and use the distance at position N/4, representing the median of the lower band, to estimate the diameter. [3]

Uses of NeuronStudio

NeuronStudio is mainly used for quantitative morphological analysis of Dendritic spines. The biggest advantage of using NeuronStudio-based tracing of dendrites and spines is that the methods based on automatic tracing are not sensitive to the drift or movement of dendrites, because each dendrite can be separately traced in every stack. This allows to compare spines from very long recordings or more separated time points. As the NeuronStudio analysis results in accurate 3D measurements of the width of spine heads and spine length, this method is useful in detecting changes in spine morphology. Further, the use of computer-based measuring enables the analysis of hundreds of spines in a relatively short time. [4]

See also

Related Research Articles

<span class="mw-page-title-main">Dendrite</span> Small projection on a neuron that receives signals

A dendrite or dendron is a branched protoplasmic extension of a nerve cell that propagates the electrochemical stimulation received from other neural cells to the cell body, or soma, of the neuron from which the dendrites project. Electrical stimulation is transmitted onto dendrites by upstream neurons via synapses which are located at various points throughout the dendritic tree.

Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. Developed in the early 1980s by Robert M. Gray, it was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. In simpler terms, vector quantization chooses a set of points to represent a larger set of points.

<span class="mw-page-title-main">Self-organizing map</span> Machine learning technique useful for dimensionality reduction

A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher-dimensional data set while preserving the topological structure of the data. For example, a data set with variables measured in observations could be represented as clusters of observations with similar values for the variables. These clusters then could be visualized as a two-dimensional "map" such that observations in proximal clusters have more similar values than observations in distal clusters. This can make high-dimensional data easier to visualize and analyze.

<span class="mw-page-title-main">Deconvolution</span> Reconstruction of a filtered signal

In mathematics, deconvolution is the inverse of convolution. Both operations are used in signal processing and image processing. For example, it may be possible to recover the original signal after a filter (convolution) by using a deconvolution method with a certain degree of accuracy. Due to the measurement error of the recorded signal or image, it can be demonstrated that the worse the signal-to-noise ratio (SNR), the worse the reversing of a filter will be; hence, inverting a filter is not always a good solution as the error amplifies. Deconvolution offers a solution to this problem.

<span class="mw-page-title-main">Dendritic spine</span> Small protrusion on a dendrite that receives input from a single axon

A dendritic spine is a small membranous protrusion from a neuron's dendrite that typically receives input from a single axon at the synapse. Dendritic spines serve as a storage site for synaptic strength and help transmit electrical signals to the neuron's cell body. Most spines have a bulbous head, and a thin neck that connects the head of the spine to the shaft of the dendrite. The dendrites of a single neuron can contain hundreds to thousands of spines. In addition to spines providing an anatomical substrate for memory storage and synaptic transmission, they may also serve to increase the number of possible contacts between neurons. It has also been suggested that changes in the activity of neurons have a positive effect on spine morphology.

<span class="mw-page-title-main">Pyramidal cell</span> Projection neurons in the cerebral cortex and hippocampus

Pyramidal cells, or pyramidal neurons, are a type of multipolar neuron found in areas of the brain including the cerebral cortex, the hippocampus, and the amygdala. Pyramidal cells are the primary excitation units of the mammalian prefrontal cortex and the corticospinal tract. One of the main structural features of the pyramidal neuron is the conic shaped soma, or cell body, after which the neuron is named. Other key structural features of the pyramidal cell are a single axon, a large apical dendrite, multiple basal dendrites, and the presence of dendritic spines.

<span class="mw-page-title-main">Golgi's method</span> Silver staining technique for visualizing nervous tissue under light microscopy

Golgi's method is a silver staining technique that is used to visualize nervous tissue under light microscopy. The method was discovered by Camillo Golgi, an Italian physician and scientist, who published the first picture made with the technique in 1873. It was initially named the black reaction by Golgi, but it became better known as the Golgi stain or later, Golgi method.

<span class="mw-page-title-main">Octree</span> Tree data structure in which each internal node has exactly eight children, to partition a 3D space

An octree is a tree data structure in which each internal node has exactly eight children. Octrees are most often used to partition a three-dimensional space by recursively subdividing it into eight octants. Octrees are the three-dimensional analog of quadtrees. The word is derived from oct + tree. Octrees are often used in 3D graphics and 3D game engines.

An apical dendrite is a dendrite that emerges from the apex of a pyramidal cell. Apical dendrites are one of two primary categories of dendrites, and they distinguish the pyramidal cells from spiny stellate cells in the cortices. Pyramidal cells are found in the prefrontal cortex, the hippocampus, the entorhinal cortex, the olfactory cortex, and other areas. Dendrite arbors formed by apical dendrites are the means by which synaptic inputs into a cell are integrated. The apical dendrites in these regions contribute significantly to memory, learning, and sensory associations by modulating the excitatory and inhibitory signals received by the pyramidal cells.

<span class="mw-page-title-main">Blind deconvolution</span>

In electrical engineering and applied mathematics, blind deconvolution is deconvolution without explicit knowledge of the impulse response function used in the convolution. This is usually achieved by making appropriate assumptions of the input to estimate the impulse response by analyzing the output. Blind deconvolution is not solvable without making assumptions on input and impulse response. Most of the algorithms to solve this problem are based on assumption that both input and impulse response live in respective known subspaces. However, blind deconvolution remains a very challenging non-convex optimization problem even with this assumption.

Neuromorphology is the study of nervous system form, shape, and structure. The study involves looking at a particular part of the nervous system from a molecular and cellular level and connecting it to a physiological and anatomical point of view. The field also explores the communications and interactions within and between each specialized section of the nervous system. Morphology is distinct from morphogenesis. Morphology is the study of the shape and structure of biological organisms, while morphogenesis is the study of the biological development of the shape and structure of organisms. Therefore, neuromorphology focuses on the specifics of the structure of the nervous system and not the process by which the structure was developed. Neuromorphology and morphogenesis, while two different entities, are nonetheless closely linked.

The synaptotropic hypothesis, also called the synaptotrophic hypothesis, is a neurobiological hypothesis of neuronal growth and synapse formation. The hypothesis was first formulated by J.E. Vaughn in 1988, and remains a focus of current research efforts. The synaptotropic hypothesis proposes that input from a presynaptic to a postsynaptic cell eventually can change the course of synapse formation at dendritic and axonal arbors. This synapse formation is required for the development of neuronal structure in the functioning brain.

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity, which requires the signal to be sparse in some domain. The second one is incoherence, which is applied through the isometric property, which is sufficient for sparse signals. Compressed sensing has applications in, for example, MRI where the incoherence condition is typically satisfied.

In various science/engineering applications, such as independent component analysis, image analysis, genetic analysis, speech recognition, manifold learning, and time delay estimation it is useful to estimate the differential entropy of a system or process, given some observations.

Sholl analysis is a method of quantitative analysis commonly used in neuronal studies to characterize the morphological characteristics of an imaged neuron, first used to describe the differences in the visual and motor cortices of cats in the early 1950s. Sholl was interested in comparing the morphology of different types of neurons, such as the star-shaped stellate cells and the cone-shaped pyramidal cells, and of different locations in the dendritic field of the same type of neurons, such as basal and apical processes of the pyramidal neuron. He looked at dendritic length and diameter and also the number of cells per volume.

Compartmental modelling of dendrites deals with multi-compartment modelling of the dendrites, to make the understanding of the electrical behavior of complex dendrites easier. Basically, compartmental modelling of dendrites is a very helpful tool to develop new biological neuron models. Dendrites are very important because they occupy the most membrane area in many of the neurons and give the neuron an ability to connect to thousands of other cells. Originally the dendrites were thought to have constant conductance and current but now it has been understood that they may have active Voltage-gated ion channels, which influences the firing properties of the neuron and also the response of neuron to synaptic inputs. Many mathematical models have been developed to understand the electric behavior of the dendrites. Dendrites tend to be very branchy and complex, so the compartmental approach to understand the electrical behavior of the dendrites makes it very useful.

Neuronal tracing, or neuron reconstruction is a technique used in neuroscience to determine the pathway of the neurites or neuronal processes, the axons and dendrites, of a neuron. From a sample preparation point of view, it may refer to some of the following as well as other genetic neuron labeling techniques,

Vaa3D is an Open Source visualization and analysis software suite created mainly by Hanchuan Peng and his team at Janelia Research Campus, HHMI and Allen Institute for Brain Science. The software performs 3D, 4D and 5D rendering and analysis of very large image data sets, especially those generated using various modern microscopy methods, and associated 3D surface objects. This software has been used in several large neuroscience initiatives and a number of applications in other domains. In a recent Nature Methods review article, it has been viewed as one of the leading open-source software suites in the related research fields. In addition, research using this software was awarded the 2012 Cozzarelli Prize from the National Academy of Sciences.

Patch-sequencing (patch-seq) is a modification of patch-clamp technique that combines electrophysiological, transcriptomic and morphological characterization of individual neurons. In this approach, the neuron's cytoplasm is collected and processed for RNAseq after electrophysiological recordings are performed on it. The cell is simultaneously filled with a dye that allows for subsequent morphological reconstruction.

References

  1. Rodriguez, A.; Ehlenberger, D.; Kelliher, K.; Einstein, M.; Henderson, S.C.; Morrison, J.H.; Hof, P.R.; Wearne, S.L. (2003). "Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images". Methods. 30 (1): 94–105. doi:10.1016/s1046-2023(03)00011-2. PMID   12695107.
  2. Wearne, S.L.; Rodriguez, A.; Ehlenberger, D.B.; Rocher, A.B.; Henderson, S.C.; Hof, P.R. (2005). "New techniques for imaging, digitization and analysis of three-dimensional neuronal morphology on multiple scales". Neuroscience. 136 (3): 661–680. CiteSeerX   10.1.1.329.9265 . doi:10.1016/j.neuroscience.2005.05.053. PMID   16344143. S2CID   16510030.
  3. Rodriguez, A.; Ehlenberger, D.; Hof, P.R.; Wearne, S.L. (2006). "Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser-scanning microscopy images". Nature Protocols. 1 (4): 2156–2161. doi:10.1038/nprot.2006.313. PMID   17487207. S2CID   16141708.
  4. Bertling, Enni; Ludwig, Anastasia; Koskinen, Mikko; Hotulainen, Pirta (2012). "Methods for Three-Dimensional Analysis of Dendritic Spine Dynamics". In Conn, P. Michael (ed.). Imaging and Spectroscopic Analysis of Living Cells: Imaging Live Cells in Health and Disease (PDF). Methods in Enzymology. Vol. 506. Elsevier. pp. 391–406. doi:10.1016/B978-0-12-391856-7.00043-3. ISBN   978-0-12-391856-7. ISSN   0076-6879. PMID   22341234 . Retrieved 2021-07-14.