@article {67289, title = {CalmAn: An open source tool for scalable Calcium Imaging data Analysis}, journal = {bioRxiv}, year = {2018}, month = {06/2018}, abstract = {

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

}, doi = {10.1101/339564}, url = {https://www.biorxiv.org/content/early/2018/06/05/339564}, author = {Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and Kalfon, J{\'e}r{\'e}mie and Koay, Sue Ann and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L. and Zhou, Pengcheng and Tank, David W. and Chklovskii, Dmitri and Pnevmatikakis, Eftychios A.} } @article {6676, title = {Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries.}, journal = {IEEE Transactions on Medical Imaging}, volume = {32}, year = {2013}, month = {2013 Aug 2}, pages = {2179-88}, abstract = {

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

}, issn = {1558-254X}, doi = {10.1109/TMI.2013.2276018}, author = {Hu, Tao and Nunez-Iglesias, Juan and Vitaladevuni, Shiv and Scheffer, Lou and Xu, Shan and Bolorizadeh, Mehdi and Hess, Harald and Fetter, Richard and Chklovskii, Dmitri} } @proceedings {6535, title = {Tracking multiple neurons on worm images.}, volume = {8669}, year = {2013}, month = {2013 Mar}, pages = {86692P}, publisher = {SPIE}, address = {Lake Buena Vista (Orlando Area), Florida, USA}, abstract = {

We are interested in establishing the correspondence between neuron activity and body curvature during various movements of C. Elegans worms. Given long sequences of images, specifically recorded to glow when the neuron is active, it is required to track all identifiable neurons in each frame. The characteristics of the neuron data, e.g., the uninformative nature of neuron appearance and the sequential ordering of neurons, renders standard single and multi-object tracking methods either ineffective or unnecessary for our task. In this paper, we propose a multi-target tracking algorithm that correctly assigns each neuron to one of several candidate locations in the next frame preserving shape constraint. The results demonstrate how the proposed method can robustly track more neurons than several existing methods in long sequences of images.

}, doi = {10.1117/12.2000087}, author = {Paraq, Toufiq and Butler, Victoria and Chklovskii, Dmitri} } @article {4325, title = {High resolution segmentation of neuronal tissues from low depth-resolution EM imagery.}, journal = {8th International Conference of Energy Minimization Methods in Computer Vision and Pattern Recognition Energy Minimization Methods in Computer Vision and Pattern Recognition}, volume = {6819}, year = {2011}, month = {2011}, pages = {261-72}, abstract = {

The challenge of recovering the topology of massive neuronal circuits can potentially be met by high throughput Electron Microscopy (EM) imagery. Segmenting a 3-dimensional stack of EM images into the individual neurons is difficult, due to the low depth-resolution in existing high-throughput EM technology, such as serial section Transmission EM (ssTEM). In this paper we propose methods for detecting the high resolution locations of membranes from low depth-resolution images. We approach this problem using both a method that learns a discriminative, over-complete dictionary and a kernel SVM. We test this approach on tomographic sections produced in simulations from high resolution Focused Ion Beam (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.

}, issn = {978-3-642-23093-6}, author = {Glasner, Daniel and Hu, Tao and Nunez-Iglesias, Juan and Scheffer, Lou and Xu, CS and Hess, Harald and Fetter, Richard and Chklovskii, Dmitri and Basri, Ronen} } @article {4327, title = {Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain.}, journal = {Current Biology}, volume = {21}, year = {2011}, month = {2011 Dec}, pages = {2000-5}, abstract = {

Wiring economy has successfully explained the individual placement of neurons in simple nervous systems like that of Caenorhabditis elegans [1-3] and the locations of coarser structures like cortical areas in complex vertebrate brains [4]. However, it remains unclear whether wiring economy can explain the placement of individual neurons in brains larger than that of C.\ elegans. Indeed, given the greater number of neuronal interconnections in larger brains, simply minimizing the length of connections results in unrealistic configurations, with multiple neurons occupying the same position in space. Avoiding such configurations, or volume exclusion, repels neurons from each other, thus counteracting wiring economy. Here we test whether wiring economy together with volume exclusion can explain the placement of neurons in a module of the Drosophila melanogaster brain known as lamina cartridge [5-13]. We used newly developed techniques for semiautomated reconstruction from serial electron microscopy (EM) [14] to obtain the shapes of neurons, the location of synapses, and the resultant synaptic connectivity. We show that wiring length minimization and volume exclusion together can explain the structure of the lamina microcircuit. Therefore, even in brains larger than that of C.\ elegans, at least for some circuits, optimization can play an important role in individual neuron placement.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2011.10.022}, author = {Rivera-Alba, Marta and Vitaladevuni, Shiv N. and Mischenko, Yuriy and Lu, Zhiyuan and Takemura, Shin-ya and Scheffer, Lou and Meinertzhagen, Ian and Chklovskii, Dmitri and Polavieja, Gonzalo} } @article {1250, title = {Geometric and functional organization of cortical circuits.}, journal = {Nature Neuroscience}, volume = {8}, year = {2005}, month = {2005 Jun}, pages = {782-90}, abstract = {

Can neuronal morphology predict functional synaptic circuits? In the rat barrel cortex, {\textquoteright}barrels{\textquoteright} and {\textquoteright}septa{\textquoteright} delineate an orderly matrix of cortical columns. Using quantitative laser scanning photostimulation we measured the strength of excitatory projections from layer 4 (L4) and L5A to L2/3 pyramidal cells in barrel- and septum-related columns. From morphological reconstructions of excitatory neurons we computed the geometric circuit predicted by axodendritic overlap. Within most individual projections, functional inputs were predicted by geometry and a single scale factor, the synaptic strength per potential synapse. This factor, however, varied between projections and, in one case, even within a projection, up to 20-fold. Relationships between geometric overlap and synaptic strength thus depend on the laminar and columnar locations of both the pre- and postsynaptic neurons, even for neurons of the same type. A large plasticity potential appears to be incorporated into these circuits, allowing for functional {\textquoteright}tuning{\textquoteright} with fixed axonal and dendritic arbor geometry.

}, keywords = {Animals, Axons, Brain Mapping, Cell Shape, Dendrites, Electric Stimulation, Excitatory Postsynaptic Potentials, Image Cytometry, Lasers, Lysine, Neural Pathways, Neuronal Plasticity, Organ Culture Techniques, Photic Stimulation, Pyramidal Cells, Rats, Somatosensory Cortex, Sprague-Dawley, Synapses, Synaptic Transmission, Ultraviolet Rays}, issn = {1097-6256}, doi = {10.1016/j.tins.2005.05.006}, author = {Shepherd, Gordon M G and Stepanyants, Armen and Bureau, Ingrid and Chklovskii, Dmitri and Svoboda, Karel} }