Sparse reconstruction of brain circuits: Or, how to survive without a microscopic connectome

Costa, Nuno Maçarico da; Martin, Kevan A

doi:10.1016/j.neuroimage.2013.04.054

Cited by 29 publications

(17 citation statements)

References 102 publications

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“…This idea underpins theoretical approaches to understanding the brain (Braitenberg and Schuz, 1998;da Costa and Martin, 2013). Explaining synaptic connectivity by physical overlap is an attractive idea because of the obviously laminated organization of many regions of the brain including the cerebral cortex.…”

Section: Discussionmentioning

confidence: 99%

Saturated Reconstruction of a Volume of Neocortex

Kasthuri

Hayworth

Berger

et al. 2015

Cell

954

974

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We describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and non-synaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters' rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.

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Section: Discussionmentioning

confidence: 99%

Saturated Reconstruction of a Volume of Neocortex

Kasthuri

Hayworth

Berger

et al. 2015

Cell

954

974

View full text Add to dashboard Cite

show abstract

“…If precise connectivity can be inferred from geometric overlap of cells reconstructed in separate tissue blocks using standard approaches, then dense microscale reconstruction might be unnecessary (da Costa and Martin, 2013). In this view, efforts should instead focus on developing a complete catalog of cell types, statistics that capture fine details of morphometric variations, and computational strategies to properly register thousands of cells in 3D and ultimately calculate the synaptic network.…”

Section: Local Connectivity In Neural Circuits: Random or Structured?mentioning

confidence: 99%

Input clustering and the microscale structure of local circuits

DeBello

McBride

Nichols

et al. 2014

Front. Neural Circuits

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The recent development of powerful tools for high-throughput mapping of synaptic networks promises major advances in understanding brain function. One open question is how circuits integrate and store information. Competing models based on random vs. structured connectivity make distinct predictions regarding the dendritic addressing of synaptic inputs. In this article we review recent experimental tests of one of these models, the input clustering hypothesis. Across circuits, brain regions and species, there is growing evidence of a link between synaptic co-activation and dendritic location, although this finding is not universal. The functional implications of input clustering and future challenges are discussed.

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“…Such partial or complete precise wiring diagrams have been termed a “connectome” (Sporns et al, 2005; Jarrell et al, 2013; Sporns, 2013). Given the massive numbers and morpho-functional diversity of neurons, as well as 3D intricacy and submicron size of their connections, and the fact that axons often extend to remote brain or body regions, the technical challenges involved in tracing connectomes with cellular resolution are staggering (de Costa and Martin, 2013). In fact, the only complete connectome produced to date is that of the small worm Caenorhabditis elegans , which has just 302 neurons of 118 morpho-functional types, which form about 5000 synapses between them.…”

Section: Introductionmentioning

confidence: 99%

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

2016

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Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. “Connectomics” is the emerging subfield of neuroanatomy explicitly aimed at elucidating the wiring of brain networks with cellular resolution and a quantified accuracy. Such data are indispensable for realistic modeling of brain circuitry and function. A connectomic analysis, therefore, needs to identify and measure the soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network. However, because of the submicron caliber, 3D complexity, and high packing density of most such structures, as well as the fact that axons frequently extend over long distances to make synapses in remote brain regions, creating connectomic maps is technically challenging and requires multi-scale approaches, Such approaches involve the combination of the most sensitive cell labeling and analysis methods available, as well as the development of new ones able to resolve individual cells and synapses with increasing high-throughput. In this review, we provide an overview of recently introduced high-resolution methods, which researchers wanting to enter the field of connectomics may consider. It includes several molecular labeling tools, some of which specifically label synapses, and covers a number of novel imaging tools such as brain clearing protocols and microscopy approaches. Apart from describing the tools, we also provide an assessment of their qualities. The criteria we use assess the qualities that tools need in order to contribute to deciphering the key levels of circuit organization. We conclude with a brief future outlook for neuroanatomic research, computational methods, and network modeling, where we also point out several outstanding issues like structure–function relations and the complexity of neural models.

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Sparse reconstruction of brain circuits: Or, how to survive without a microscopic connectome

Cited by 29 publications

References 102 publications

Saturated Reconstruction of a Volume of Neocortex

Saturated Reconstruction of a Volume of Neocortex

Input clustering and the microscale structure of local circuits

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

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