Evidence map›Paper›PMID 35873809›Full record

ArticleFrontiers in neuroscience2022

Transient Motion Classification Through Turbid Volumes

Shiqi Xu, Wenhui Liu, Xi Yang, Joakim Jönsson, Ruobing Qian, Paul McKee, Kanghyun Kim, Pavan Chandra Konda, Kevin C Zhou, Lucas Kreiß and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Shiqi XuDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Wenhui LiuDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Xi YangDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Joakim JönssonDivision of Combustion Physics, Department of Physics, Lund University, Lund, Sweden.
Ruobing QianDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Paul McKeeDepartment of Psychology and Neuroscience, Duke University, Durham, NC, United States.
Kanghyun KimDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Pavan Chandra KondaDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Kevin C ZhouDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Lucas KreißDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Haoqian WangTsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Edouard BerrocalDivision of Combustion Physics, Department of Physics, Lund University, Lund, Sweden.
Scott A HuettelDepartment of Psychology and Neuroscience, Duke University, Durham, NC, United States.
Roarke HorstmeyerDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed

Indexed as

contrastive learningdiffuse correlationmultimode fiberneurobehaviorself-supervised learningSPAD arrayzero-shot learning

Identifiers

PMID35873809
PMCPMC9304989

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.