Evidence map›Paper›PMID 31467291›Full record

ArticleNature communications2019

A null model of the mouse whole-neocortex micro-connectome.

Michael W Reimann, Michael Gevaert, Ying Shi, Huanxiang Lu, Henry Markram, Eilif Muller

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Neural network embedding of functional microconnectome.Network neuroscience (Cambridge, Mass.) · 2025
    Article
  5. Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. The energy challenges of artificial superintelligence.Frontiers in artificial intelligence · 2023
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Computational Concepts for Reconstructing and Simulating Brain Tissue.Advances in experimental medicine and biology · 2022
    Article
  18. Highlights from the Era of Open Source Web-Based Tools.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
    Review
  19. Greedy low-rank algorithm for spatial connectome regression.Journal of mathematical neuroscience · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michael W ReimannBlue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland. michael.reimann@epfl.ch.ORCID http://orcid.org/0000-0003-3455-2367
Michael GevaertBlue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Ying ShiBlue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Huanxiang LuBlue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.ORCID http://orcid.org/0000-0003-3377-9359
Henry MarkramBlue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Eilif MullerBlue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.

Indexed as

Neural Networks, ComputerAnimalsBrainConnectomeMiceModels, AnimalModels, StatisticalNeocortexNerve NetNeurons

Identifiers

PMID31467291
PMCPMC6715727

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.