Evidence map›Paper›PMID 27608442›Full record

ArticleIEEE transactions on bio-medical engineering2018

Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

Xi Jiang, Xiang Li, Jinglei Lv, Shijie Zhao, Shu Zhang, Wei Zhang, Tuo Zhang, Junwei Han, Lei Guo, Tianming Liu

Abstract read
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Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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  12. Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network.IEEE journal of biomedical and health informatics · 2019
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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

10 authors.

Xi Jiang
Xiang Li
Jinglei Lv
Shijie Zhao
Shu Zhang
Wei Zhang
Tuo Zhang
Junwei Han
Lei Guo
Tianming Liu

Funding

Assessing Large-scale Connectivities in Prenatal Cocaine Exposure Affected BrainsR01DA033393 · NIDA · UNIVERSITY OF GEORGIA · PI HU, XIAOPING P, LIU, TIANMING · 2012 to 2016
$1.6M
Assessing Large-scale Brain Connectivities in Mild Cognitive ImpairmentR01AG042599 · NIA · UNIVERSITY OF GEORGIA · PI LIU, TIANMING · 2013 to 2017
$1.4M
NIA NIH HHS R01 AG042599NIDA NIH HHS R01 DA033393
6 · The paper itself

Abstract

objectiveVarious studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on functional magnetic resonance imaging (fMRI) has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown.

methodsTo address the two issues, we propose a novel computational framework of group-wise sparse representation of whole-brain fMRI temporal segments to assess the temporal dynamic spatial patterns of SOPFNs that are consistent across different subjects.

resultsExperimental results based on the recently publicly released Human Connectome Project grayordinate task fMRI data demonstrate that meaningful SOPFNs exhibiting dynamic spatial patterns across different time periods are effectively and robustly identified based on the reconstructed concurrent functional networks via the proposed framework. Specifically, those SOPFNs locate significantly more on gyral regions than on sulcal regions across different time periods.

conclusionThese results reveal novel functional architecture of cortical gyri and sulci. SIGNIFICANCE: Moreover, these results help better understand functional dynamics mechanisms of cerebral cortex in the future.

Indexed as

AlgorithmsBrain MappingCerebral CortexHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingNerve NetSupervised Machine Learning

Identifiers

PMID27608442
PMCPMC5988586

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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.