Evidence map›Paper›PMID 36197624›Full record

ArticleNeuroinformatics2023

Bayesian Coherence Analysis for Microcircuit Structure Learning.

Rong Chen

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Rong ChenDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 100 N Greene, Baltimore, MD, 21201, USA. rchen@som.umaryland.edu.

Funding

Dissemination of Real-time Neural Decoding for Cellular imaging LaboratoryR01NS110421 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI BHATTACHARYYA, SHUVRA S, CHEN, RONG · 2018 to 2022
$860k
Clear Volume Imaging with Machine Learning: a novel tool to identify brain-wide neuronal ensembles of opioid relapse in rat modelsUG3DA053802 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI CHEN, RONG, GOLDEN, SAM · 2021 to 2022
$423k
NIDA NIH HHS UG3 DA053802NINDS NIH HHS R01 NS110421
6 · The paper itself

Abstract

Functional microcircuits model the coordinated activity of neurons and play an important role in physiological computation and behaviors. Most existing methods to learn microcircuit structures are correlation-based and often generate dense microcircuits that cannot distinguish between direct and indirect association. We treat microcircuit structure learning as a Markov blanket discovery problem and propose Bayesian Coherence Analysis (BCA) which utilizes a Bayesian network architecture called Bayesian network with inverse-tree structure to efficiently and effectively detect Markov blankets for high-dimensional neural activity data. BCA achieved balanced sensitivity and specificity on simulated data. For the real-world anterior lateral motor cortex study, BCA identified microcircuit subtypes that predicted trial types with an accuracy of 0.92. BCA is a powerful method for microcircuit structure learning.

Indexed as

NeuronsBayes TheoremBayesian networkMarkov blanketMarkov networkMicrocircuitStructure learning

Identifiers

PMID36197624
PMCPMC9931807

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.