Evidence map›Paper›PMID 36713289›Full record

ArticleFrontiers in neuroinformatics2022

Predicting

Taehoon Kim, Dexiong Chen, Philipp Hornauer, Vishalini Emmenegger, Julian Bartram, Silvia Ronchi, Andreas Hierlemann, Manuel Schröter, Damian Roqueiro

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroinformatics, 2022. 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
1.1field-weighted citation impact, top 26% 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, 6 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 2 institutions in 1 country.

Taehoon KimBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Dexiong ChenMachine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Philipp HornauerBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Vishalini EmmeneggerBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Julian BartramBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Silvia RonchiBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Andreas HierlemannBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Manuel SchröterBioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Damian RoqueiroMachine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
ETH Zurich · CHSIB Swiss Institute of Bioinformatics · CH

Funding

European Research Council 694829Swiss National Science Foundation 188910
6 · The paper itself

Abstract

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA

Indexed as

extracellular electrophysiologyGraph Neural Networkin vitro neural networkmachine learningpharmacological perturbationsingle neuron activity

Identifiers

PMID36713289
PMCPMC9874697
OpenAlexW4315649530

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.