Evidence map›Paper›PMID 35502315›Full record

ArticleInternational IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering2021

Inferring Morphology of a Neuron from In Vivo LFP Data.

Ziao Chen, Dan Dopp, Drew B Headley, Satish S Nair

Open access · greenAbstract read
In one paragraph

Article in International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 46% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Inference of alveolar capillary network connectivity from blood flow dynamics.American journal of physiology. Lung cellular and molecular physiology · 2024
    Article
  2. Inferring Pyramidal Neuron Morphology using EAP Data.International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering · 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

4 authors at 2 institutions in 1 country.

Ziao ChenElectrical Engineering and Computer Science, University of Missouri, Columbia MO 65211.
Dan DoppElectrical Engineering and Computer Science, University of Missouri, Columbia MO 65211.
Drew B HeadleyCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.
Satish S NairElectrical Engineering and Computer Science, University of Missouri, Columbia MO 65211.
University of Missouri · USRutgers, The State University of New Jersey · US

Funding

CRCNS: Optimization of closed-loop control of gamma oscillationsR01MH122023 · NIMH · UNIVERSITY OF MISSOURI-COLUMBIA · PI NAIR, SATISH S · 2019 to 2023
$1.4M
NIMH NIH HHS R01 MH122023
6 · The paper itself

Abstract

We propose a computational pipeline that uses biophysical modeling and sequential neural posterior estimation algorithm to infer the position and morphology of single neurons using multi-electrode in vivo extracellular voltage recordings. In this inverse modeling scheme, we designed a generic biophysical single neuron model with stylized morphology that had adjustable parameters for the dimensions of the soma, basal and apical dendrites, and their location and orientations relative to the multi-electrode probe. Preliminary results indicate that the proposed methodology can infer up to eight neuronal parameters well. We highlight the issues involved in the development of the novel pipeline and areas for further improvement.

Identifiers

PMID35502315
PMCPMC9040040
OpenAlexW3172278956

What OpenQuestion holds

Textmetadata
LicenceTDM
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.