Evidence map›Paper›PMID 34746623›Full record

ArticleNetwork neuroscience (Cambridge, Mass.)2021

Neuronal classification from network connectivity via adjacency spectral embedding.

Ketan Mehta, Rebecca F Goldin, David Marchette, Joshua T Vogelstein, Carey E Priebe, Giorgio A Ascoli

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Circuit analysis of theNetwork neuroscience (Cambridge, Mass.) · 2023
    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.

Ketan MehtaDepartment of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA.ORCID https://orcid.org/0000-0003-2360-7764
Rebecca F GoldinDepartment of Mathematical Sciences and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA.ORCID https://orcid.org/0000-0001-7082-0248
David MarchetteNaval Surface Warfare Center, Dahlgren, VA, USA.
Joshua T VogelsteinDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0003-2487-6237
Carey E PriebeDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Giorgio A AscoliDepartment of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA.ORCID https://orcid.org/0000-0002-0964-676X

Funding

Anatomical characterization of neuronal cell types of the mouse brainU01MH114829 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ASCOLI, GIORGIO A, DONG, HONG-WEI · 2017 to 2021
$13.9M
Generation and Description of Neuronal Morphology and ConnectivityR01NS039600 · NINDS · GEORGE MASON UNIVERSITY · PI ASCOLI, GIORGIO A · 1999 to 2024
$6.9M
NIMH NIH HHS U01 MH114829NINDS NIH HHS R01 NS039600
6 · The paper itself

Abstract

This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (≈2

Indexed as

Cell-type classificationDirected graphsExpectation maximizationNeural circuitsSpectral embeddingStochastic block models

Identifiers

PMID34746623
PMCPMC8567830

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.