Title:
A Multicore Path to Connectomics-on-Demand

Keywords:
connectomics

Abstract:
<p>Connectomics is an emerging field of neurobiology that uses cutting edge ... images. It has long been assumed that the processing of connectomics data will require mass storage and farms of CPUs and ... years. This talk shows the feasibility of designing a high-throughput connectomics- -on-demand system that runs on a multicore machine with less ... a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes. ...

Full Text:
Microsoft Word - spaa201ns-shavitSC.pdf.docxInvited Talk: A Multicore Path to Connectomics- -on-Demand Nir Shavit Massachusetts Institute of Technology and Tel-Aviv University ... Shavit Massachusetts Institute of Technology and Tel-Aviv University shanir@csail.mit.edu ABSTRACT Connectomics is an emerging field of neurobiology that uses cutting edge ... images. It has long been assumed that the processing of connectomics data will require mass storage and farms of CPUs and ... years. This talk shows the feasibility of designing a high-throughput connectomics- -on-demand system that runs on a multicore machine with less ... a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes. ...

Title:
The open connectome project data cluster: scalable analysis and vision for high-throughput neuroscience

Keywords:
connectomics

Abstract:
... well. The system was designed primarily for workloads that build <i>connectomes</i>- ---neural connectivity maps of the brain---using the parallel execution of ...

References:
V. Jain, H. S. Seung, and S. C. Turaga. Machines that learn to segment images: a crucial technology for connectomics. Current opinion in neurobiology, 20(5), 2010.
D. M. Kleissas, W. R. Gray, J. M. Burck, J. T. Vogelstein, E. Perlman, P. M. Burlina, R. Burns, and R. J. Vogelstein. CAJAL3D: toward a fully automatic pipeline for connectome estimation from high-resolution em data. In Neuroinformatics, 2012.
A. V. Reina, M. Gelbart, D. Huang, J. Lichtman, E. L. Miller, and H. Pfister. Segmentation fusion for connectomics. In International Conference on Computer Vision, 2011.
A. V. Reina, W.-K. Jeong, J. Lichtman, and H. Pfister. The connectome project: discovering the wiring of the brain. ACM Crossroads, 18(1):8--13, 2011.

Full Text:
NimbusSanL-ReguThe Open Connectome Project Data Cluster: ScalableAnalysis and Vision for High-Throughput NeuroscienceRandal Burns? ... as well. The system wasdesigned primarily for workloads that build connectomes? ?neural connectivity maps of the brain?using the parallel ex-ecution of ... and Open Science [26]. Labs contributeimaging data to the Open Connectome Project (OCP). Inexchange, OCP provides storage and analysis Web-servicesthat relieve ...
... the ultimate goal of a full reconstruction of thebrain?the human connectome or human brain map.1Figure 1: Visualization of the spatial distributionof ... in the mouse visual cortex ofBock et al. [3].The Open Connectome Project was specifically designed tobe a scalable data infrastructure for ... analysis to extract volumes, find nearestneighbors, and compute distances.The Open Connectome Project stores more than 50 uniquedata sets totaling more than ... pixel image volume: one quarter scale of the largestpublished EM connectome data [3]. This involved a clus-ter of three physical nodes ... data sets and the correspondinganalysis as use cases for Open Connectome Project services.Figure 2: Electron microscopy imaging of a mousesomatosensory cortex ...
... portal [25].7. FINAL THOUGHTSIn less than two years, The Open Connectome Projecthas evolved from a problem statement to a data manage-ment ...
systems engineers, machine learners, and big-datascientists. The Open Connectome Project aims to be theforum in which they all meet.The ... Internet bandwidth by a factor of forty. Wealso expect the Connectomics community to expand rapidly.The US President?s Office recently announced a ... bottlenecks.AcknowledgmentsThe authors would like to thank additional members of theOpen Connectome Project that contributed to this work,including Disa Mhembere and Ayushi ... Burns, andR. J. Vogelstein. CAJAL3D: toward a fully automaticpipeline for connectome estimation fromhigh-resolution em data. In Neuroinformatics, 2012.[20] Y. Li, E. ...
... A. V. Reina, W.-K. Jeong, J. Lichtman, andH. Pfister. The connectome project: discovering thewiring of the brain. ACM Crossroads, 18(1):8?13,2011.[33] D. ...

Title:
Using Network Alignment for Analysis of Connectomes: Experiences from a Clinical Dataset

Keywords:
Connectome

Abstract:
... the complex system of connections in neural systems, i.e. the connectome, , has gained a central role in neurosciences. The modeling ... a central role in neurosciences. The modeling and analysis of connectomes is therefore a growing area. Here we focus on the ... a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages, the analyzed brains are co-registered ... cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

References:
C. I. Bargmann and E. Marder. From the connectome to brain function. Nature methods, 10(6):483--490, 2013.
A. Fornito, A. Zalesky, and M. Breakspear. Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage, 80:426--444, 2013.
D. E. Meskaldji, E. Fischi-Gomez, A. Griffa, P. Hagmann, S. Morgenthaler, and J.-P. Thiran. Comparing connectomes across subjects and populations at different scales. NeuroImage, 80:416--425, 2013.
O. Sporns, G. Tononi, and R. Kötter. The human connectome: a structural description of the human brain. PLoS Comput Biol, 1(4):e42, 2005.
A. W. Toga, K. A. Clark, P. M. Thompson, D. W. Shattuck, and J. D. Van Horn. Mapping the human connectome. Neurosurgery, 71(1):1, 2012.
O. Tymofiyeva, E. Ziv, A. J. Barkovich, C. P. Hess, and D. Xu. Brain without anatomy: construction and comparison of fully network-driven structural mri connectomes. PloS one, 9(5):e96196, 2014.

Keywords:
connectome

Abstract:
... tested using our system is the exploration of the human connectome: : the network of nodes and connections that underlie the ... validation of our technology, we then exposed participants to a connectome dataset using both our system and a state-of-the-art software for ... We systematically measured participants' understanding and visual memory of the connectomic structure. Our results showed that participants retained more information about ...

References:
X. Arsiwalla, A. Betella, E. Martinez, P. Omedas, R. Zucca, and P. Verschure. The dynamic connectome: towards large-scale 3D reconstruction of brain activity in real-time. BMC Neuroscience, 14(Suppl 1):P407, 2013.
S. Gerhard, A. Daducci, A. Lemkaddem, R. Meuli, J.-P. Thiran, and P. Hagmann. The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes. Frontiers in neuroinformatics, 5(June):15, 2011.
O. Sporns. The human connectome: a complex network. Annals of the New York Academy of Sciences, 1224:109--25, Apr. 2011.
O. Sporns. The Human Connectome: Origins and Challenges. NeuroImage, Mar. 2013.
O. Sporns, G. Tononi, and R. Kötter. The Human Connectome: A Structural Description of the Human Brain. PLoS Comput Biol, 1(4):e42, 2005.

Full Text:
... we tested using our system is the exploration ofthe human connectome: : the network of nodes and connec-tions that underlie the ... comparative validation of our technology,we then exposed participants to a connectome dataset usingboth our system and a state-of-the-art software for visual-ization ... same network. We systematicallymeasured participants? understanding and visual memory ofthe connectomic structure. Our results showed that partici-pants retained more information about ... April 9-11, 2014 Laval, FranceCopyright 2014 ACM 978-1-4503-2626-1 ...$10.00.KeywordsNetwork; Graphs; Connectome; ; Immersion; Navigation, Ex-ploration; Virtual RealityCategories and Subject DescriptorsH.5.1. [Multimedia ...
... in real-time1.As a test scenario, we used the human brain connectome, ,?a comprehensive structural description of the network ofelements and connections ... Viewer, a state of the art software to visualizeand analyze connectomic data [12].2. METHODS2.1 The eXperience Induction Machine72 Floor Tiles(RGB light ... unveiling the ?neural choreography? thatunderlies neuroscience data, such as the connectome, , scien-tists can find new insights to better understand the ... of parsing GraphML files.To plot a 3D representation of the connectome network inreal-time (Figure 2) each node is associated with an ...
... For this reason, we coupled the structural represen-tation of the connectome network with iqr, an open sourcereal-time neuronal network simulator [7]. ... the resulting activity propagating through the network(Figure 4e,f,g).Similar to the connectome structural network, neuronalsystems in iqr are specified using the XML ... format andcomposed of processes, groups and connections. We con-verted the connectome dataset into an iqr system format.We mapped the brain areas ... forearm orientation, fingers position andelectrodermal response (EDR).2.3.3 PerformanceUsing the human connectome dataset as a benchmark, ourapplication in XIM reaches an average ...
... Empirical evaluation2.6.1 Sample and protocolWe compared our system to the Connectome Viewer, astate of the art software for PC to visualize ... of the art software for PC to visualize and analyzemulti-modal connectome data [12].We recruited 20 graduate students (11 females, mean age27.3 ... latest generation desktop PC, whilethe second group experienced the same connectome networkin XIM.We measured the structural understanding of the datasetusing a ... to assess the recollectionof the main structural components of the connectome such asthe brain areas, their interconnections, and their properties(e.g. most ... Duringthe sessions, participants in both conditions were exposedto the same connectome dataset without having any pre-learned knowledge of it. No training ... with demographic information and were also instructedto explore freely the connectome dataset trying to rememberas many aspects of the network as ... included 6 questions to assess the par-ticipants? understanding of the connectome dataset struc-ture. We assigned a score of 1 to questions ... inscore for the drawing task between the XIM systemand the Connectome Viewer Toolkit. The error barsrepresent the standard deviation.We conducted an ...
... XIM system (mean = 4.30 0.95 SD),as opposed to the Connectome Viewer Toolkit (mean = 2.80 1.62 SD) (Figure 5).To evaluate the ... XIM (mean = 2.5 0.7 SD), as op-posed to the Connectome Viewer Toolkit (mean = 1.5 0.97SD) (Figure 6).4. CONCLUSIONSTo address ... exploration of large network data (in this specificcase, the human connectome) ) and it provides an ecologicalform of interaction where the ... informative and insightfulways.As a test scenario, we used the human connectome datasetcomposed of approximately 28 thousand connections and 1thousand nodes.We conducted ... placed labels of anatomical brain areas.system (in XIM) to the Connectome Viewer (running on alatest generation desktop PC).First, we measured the ...
... Computer Graphics, IEEE Transactions on,14(3):551?563, 2008.[23] O. Sporns. The human connectome: : a complexnetwork. Annals of the New York Academy ofSciences, ... York Academy ofSciences, 1224:109?25, Apr. 2011.[24] O. Sporns. The Human Connectome: : Origins andChallenges. NeuroImage, Mar. 2013.[25] O. Sporns, G. Tononi, ...

Keywords:
connectomics

References:
Manuel Berning, Kevin M. Boergens, and Moritz Helmstaedter. 2015. SegEM: Efficient Image Analysis for High-Resolution Connectomics. Neuron 87, 6: 1193-1206. http://doi.org/10.1016/j.neuron.2015.09.003
Randal Burns, William Gray Roncal, Dean Kleissas, et al. 2013. The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. International Conference on Scientific and Statistical Database Management: 1-11. http://doi.org/10.1145/2484838.2484870
Daniel Haehn, Seymour Knowles-barley, Mike Roberts, et al. 2014. Design and Evaluation of Interactive Proofreading Tools for Connectomics. http://doi.org/10.1109/TVCG.2014.2346371
Moritz Helmstaedter, Kevin L Briggman, Srinivas C Turaga, Viren Jain, H Sebastian Seung, and Winfried Denk. 2013. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 7461: 168-74. http://doi.org/10.1038/nature12346
Viren Jain, H Sebastian Seung, and Srinivas C Turaga. 2010. Machines that learn to segment images: a crucial technology for connectomics. Curr Opin Neurobiol 20, 5: 653-666. http://doi.org/10.1016/j.conb.2010.07.004
Jeff William Lichtman. 2014. Brain Connectomics? Retrieved from https://www.youtube.com/watch?v=2QVy0n_rdBI
Marta Pallotto, Paul V Watkins, Boma Fubara, Joshua H Singer, and Kevin L Briggman. 2015. Extracellular space preservation aids the connectomic analysis of neural circuits. eLife. http://doi.org/10.7554/eLife.08206
Stephen M Plaza, Louis K Scheffer, and Dmitri B Chklovskii. 2014. Toward large-scale connectome reconstructions. Current opinion in neurobiology 25C: 201-210. http://doi.org/10.1016/j.conb.2014.01.019
Shin-ya Takemura, Arjun Bharioke, Zhiyuan Lu, et al. 2013. A visual motion detection circuit suggested by Drosophila connectomics. Nature 500, 7461: 175-181. Retrieved from http://dx.doi.org/10.1038/nature12450
Shin-ya Takemura, Arjun Bharioke, Zhiyuan Lu, et al. 2013. A visual motion detection circuit suggested by Drosophila connectomics. Nature 500, 7461: 175-81. http://doi.org/10.1038/nature12450

Full Text:
... of up to 40 brain sections per second. Author Keywords Connectomics; ; brain mapping; array tomography; neural circuit reconstruction; segmentation; annotation; ... User Interfaces: Input devices and strategies interfaces and presentation. INTRODUCTION Connectomics Cellular connectomics (watch a video introduction from pioneer Jeff Lichtman [29]), a ... a multimodal interaction (eyetracker and gamepad controller) approach for cellular connectomics with the aim to increase the tracing throughput of single ...
... and Moritz Helmstaedter. 2015. SegEM: Efficient Image Analysis for High-Resolution Connectomics. . Neuron 87, 6: 1193?1206. http://doi.org/10.1016/j.neuron.2015.09.003 5. Davi D Bock, ... William Gray Roncal, Dean Kleissas, et al. 2013. The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. ...
... al. 2014. Design and Evaluation of Interactive Proofreading Tools for Connectomics. . http://doi.org/10.1109/TVCG.2014.2346371 20. Moritz Helmstaedter, Kevin L Briggman, and Winfried ... Turaga, Viren Jain, H Sebastian Seung, and Winfried Denk. 2013. Connectomic reconstruction of the inner plexiform layer in the mouse retina. ... Machines that learn to segment images: a crucial technology for connectomics. . Curr Opin Neurobiol 20, 5: 653?666. http://doi.org/10.1016/j.conb.2010.07.004 26. Narayanan ... computing systems, 430. http://doi.org/10.1145/1240624.1240692 29. Jeff William Lichtman. 2014. Brain Connectomics? ? Retrieved from https://www.youtube.com/watch?v=2QVy0n_rdBI 30. Julio C Mateo. 2008. Gaze ... and Kevin L Briggman. 2015. Extracellular space preservation aids the connectomic analysis of neural circuits. eLife. http://doi.org/10.7554/eLife.08206 33. Stephen M Plaza, ... Louis K Scheffer, and Dmitri B Chklovskii. 2014. Toward large-scale connectome reconstructions. Current opinion in neurobiology 25C: 201?210. http://doi.org/10.1016/j.conb.2014.01.019 34. M ...
... al. 2013. A visual motion detection circuit suggested by Drosophila connectomics. . Nature 500, 7461: 175?181. Retrieved from http://dx.doi.org/10.1038/nature12450 43. Shin-ya ... al. 2013. A visual motion detection circuit suggested by Drosophila connectomics. . Nature 500, 7461: 175?81. http://doi.org/10.1038/nature12450 44. Srinivas C Turaga, ...

Keywords:
human connectome

References:
O. Sporns, G. Tononi, and R. Ktter, "The human connectome: A structural description of the human brain," PLoS Comput Biol, vol. 1, no. 4, p. e42, 09 2005.
D. Wu, T. Wu, Y. Shan, Y. Wang, Y. He, N. Xu, and H. Yang, "Making human connectome faster: Gpu acceleration of brain network analysis," in Proceedings of the 2010 IEEE 16th International Conference on Parallel and Distributed Systems, ser. ICPADS '10. Shanghai, China: IEEE Computer Society, 2010, pp. 593--600. {Online}. Available: http://dx.doi.org/10.1109/ICPADS.2010.105

Full Text:
... Platform; GPU Acceleration; HumanConnectome; Voxel-based Brain Network AnalysisI. INTRODUCTIONThe Human Connectome is a comprehensive structuraldescription of the connectivity of the human ...
... 2009AA01Z130).REFERENCES[1] O. Sporns, G. Tononi, and R. Ktter, ?The human connectome: : Astructural description of the human brain,? PLoS Comput Biol, ... Y. Wang, Y. He, N. Xu, and H. Yang, ?Makinghuman connectome faster: Gpu acceleration of brain network analysis,?in Proceedings of the ...

Title:
Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction

Keywords:
brain connectome prediction

Abstract:
... deep neural network architecture with dynamically programmed layers for brain connectome prediction problem. Understanding the brain connectome structure is a very interesting and a challenging problem. It ... of Convolutional layer and a Recurrent layer for predicting the connectome of neurons based on their time-series of activation data. The ...

References:
J. G. Orlandi, B. Ray, D. Battaglia, I. Guyon, V. Lemaire, M. Saeed, J. Soriano, A. Statnikov, and O. Stetter. First connectomics challenge: From imaging to connectivity.
A. Sutera, A. Joly, V. François-Lavet, Z. A. Qiu, G. Louppe, D. Ernst, and P. Geurts. Simple connectome inference from partial correlation statistics in calcium imaging. arXiv preprint arXiv:1406.7865, 2014.
D. C. Van Essen, S. M. Smith, D. M. Barch, T. E. Behrens, E. Yacoub, K. Ugurbil, W.-M. H. Consortium, et al. The wu-minn human connectome project: an overview. Neuroimage, 80:62--79, 2013.

Full Text:
Deep Learning Architecture with Dynamically ProgrammedLayers for Brain Connectome PredictionVivek VeeriahUniversity of Central Florida4000 Central Florida BlvdOrlando, FL 32816vivekveeriah@knights.ucf.eduRohit ... of Convolutional layer and a Re-current layer for predicting the connectome of neurons basedon their time-series of activation data. The key ... theyare inaccurate, inefficient or require invasive approaches.Connectomics is study of connectomes, , which are mapsof connection within an organism?s nervous system. ... from patterns of neural activityare not new. The aim of connectomics is to derive the de-tailed structure of the entire neural ... active research efforts in therecent years to produce and analyze connectome datasets atmeso and macro scales. These involve non-invasive imagingtechniques of ... Generalized Linear Model (GLM) [9]kernels which were a measure of connectome weights of neu-rons. Their work builds rigorously on the study ...
... time series of the activations of neurons to discoverthe brain connectome. . The problem is formally defined inSection 3, followed by ... achieve this goal of understanding the brain, thefirst ChaLearn Neural Connectomics Challenge was held.The dataset released under this challenge consisted of ... The local activations are responsible forthrowing the light on brain connectome structure. Theselocal activation data are then used for calculating the ...
... to the classification process.A popular method for inferring the brain connectomes isthe one done by Stetter et. al. [15]. They explore ... competition very challenging. The onlyinformation available to decide the brain connectome aretime-series activations, and a training set D = {(x,x?), y}on ... this paper, we propose a novel architecture for predict-ing the connectomes based on the time-series neuronal acti-vation data. The proposed architecture ...
... ad-vance the state of the art in prediction of brain connectomes. .1This boundedness condition can be satisfied by setting theoutput activation ...
... of the state of theart method used for predicting the connectomes of thebrain neurons.? Partial Correlation Statistics [16]: This method con-sists ...
... proposed a novel deep learning algorithm for predict-ing the brain connectomes based on the time-series activa-tions of brain neurons. The proposed ...
... Lemaire, M. Saeed, J. Soriano, A. Statnikov, andO. Stetter. First connectomics challenge: Fromimaging to connectivity.[13] J. G. Orlandi, O. Stetter, J. ...