Putting neurons on the map

Eisenstein, Michael

doi:10.1038/4611149a

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…opened by the recent finding of the group of TC Südhof that neuroligin-3 mutations in autism are associated with altered 3D dendritic trees and thus altered neuronal connectivity10. Conceptional understanding of neuronal diversity and connectivity was revolutionized by this type of 3D analysis of dendritic trees11. Cortical neurons in the brain are classified by the 3D structure of their dendritic trees1213.…”

mentioning

confidence: 99%

Novel 3D Microscopic Analysis of Human Placental Villous Trees Reveals Unexpected Significance of Branching Angles

Haeussner

Buehlmeyer

Schmitz

et al. 2014

Sci Rep

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The villous trees of human placentas delineate the fetomaternal border and are complex three-dimensional (3D) structures. Thus far, they have primarily been analyzed as thin, two-dimensional (2D) histological sections. However, 2D sections cannot provide access to key aspects such as branching nodes and branch order. Using samples taken from 50 normal human placentas at birth, in the present study we show that analysis procedures for 3D reconstruction of neuronal dendritic trees can also be used for analyzing trees of human placentas. Nodes and their branches (e.g., branching hierarchy, branching angles, diameters, and lengths of branches) can be efficiently measured in whole-mount preparations of isolated villous trees using high-end light microscopy. Such data differ qualitatively from the data obtainable from histological sections and go substantially beyond the morphological horizon of such histological data. Unexpectedly, branching angles of terminal branches of villous trees varied inversely with the fetoplacental weight ratio, a widely used clinical parameter. Since branching angles have never before been determined in the human placenta, this result requires further detailed studies in order to fully understand its impact.

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confidence: 99%

Novel 3D Microscopic Analysis of Human Placental Villous Trees Reveals Unexpected Significance of Branching Angles

Haeussner

Buehlmeyer

Schmitz

et al. 2014

Sci Rep

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“…Without consensus on which modeling approach is most functionally appropriate for whole-brain simulations, any estimate will require an assumption-based approach to whole-brain simulation. Second, connectivity in the brain is not fully known even among animal models, where the quest for a complete connectome has just begun (Eisenstein, 2009). Finally, the appropriateness of a simulation's duration for studying brain function depends on the goals of the study: even with a consensus approach and known connectivity, a brain simulation needs to proceed for some simulated duration that will yield a valuable scientific result.…”

Section: Discussionmentioning

confidence: 99%

An Ultrascalable Solution to Large-scale Neural Tissue Simulation

Kozloski¹,

Wagner²

2011

Front. Neuroinform.

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Neural tissue simulation extends requirements and constraints of previous neuronal and neural circuit simulation methods, creating a tissue coordinate system. We have developed a novel tissue volume decomposition, and a hybrid branched cable equation solver. The decomposition divides the simulation into regular tissue blocks and distributes them on a parallel multithreaded machine. The solver computes neurons that have been divided arbitrarily across blocks. We demonstrate thread, strong, and weak scaling of our approach on a machine with more than 4000 nodes and up to four threads per node. Scaling synapses to physiological numbers had little effect on performance, since our decomposition approach generates synapses that are almost always computed locally. The largest simulation included in our scaling results comprised 1 million neurons, 1 billion compartments, and 10 billion conductance-based synapses and gap junctions. We discuss the implications of our ultrascalable Neural Tissue Simulator, and with our results estimate requirements for a simulation at the scale of a human brain.

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“…The improved resolution and amount of detail afforded by emerging electron microscopy (EM) techniques, such as serial block scanning EM (SBEM) (Denk and Horstmann, 2004; Leighton, 1981), is enabling researchers to explore scientific questions pertaining to morphology and network connectivity that were previously impossible (Eisenstein, 2009). SBEM techniques, coupled to new staining protocols (Deerinck et al , 2010), are able to reveal cell boundaries, sites such as synapses, and many intracellular components, such as synaptic vesicles and mitochondria.…”

Section: Introductionmentioning

confidence: 99%

DP2: Distributed 3D image segmentation using micro-labor workforce

2013

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Summary: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon’s Mechanical Turk system provides the labor in a scalable manner.Availability and implementation: Python-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/.Contact: rgiuly@ucsd.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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Putting neurons on the map

Abstract: After a long lull, powerful new technologies are putting the charting of brain circuitry back on neuroscientists' agenda. Michael Eisenstein explores the challenge of mapping the mammalian mind.

Cited by 17 publications

References 9 publications

Novel 3D Microscopic Analysis of Human Placental Villous Trees Reveals Unexpected Significance of Branching Angles

Novel 3D Microscopic Analysis of Human Placental Villous Trees Reveals Unexpected Significance of Branching Angles

An Ultrascalable Solution to Large-scale Neural Tissue Simulation

DP2: Distributed 3D image segmentation using micro-labor workforce

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