Evidence map›Paper›PMID 34337613›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020

A Computational Framework for Dissociating Development-Related from Individually Variable Flexibility in Regional Modularity Assignment in Early Infancy.

Mayssa Soussia, Xuyun Wen, Zhen Zhou, Bing Jin, Tae-Eui Kam, Li-Ming Hsu, Zhengwang Wu, Gang Li, Li Wang, Islem Rekik and 3 more

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

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. 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
1.3field-weighted citation impact, top 17% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  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

13 authors at 3 institutions in 2 countries.

Mayssa SoussiaDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Xuyun WenDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Zhen ZhouDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Bing JinDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Tae-Eui KamDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Li-Ming HsuDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Zhengwang WuDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Gang LiDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Li WangDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Islem RekikBASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
Weili LinDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Dinggang ShenDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Han ZhangDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Imaging Center · USUniversity of North Carolina at Chapel Hill · USIstanbul Technical University · TR

Funding

UNC/UMN Baby Connectome ProjectU01MH110274 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ELISON, JED THOMAS, GILMORE, JOHN HORACE · 2016 to 2019
$4.5M
Continued Development of Infant Brain Analysis ToolsR01MH117943 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG, WANG, LI · 2018 to 2022
$2.3M
Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRIR01MH116225 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2018 to 2022
$1.9M
NIMH NIH HHS R01 MH116225NIMH NIH HHS R01 MH117943NIMH NIH HHS U01 MH110274
6 · The paper itself

Abstract

Functional brain development in early infancy is a highly dynamic and complex process. Understanding each brain region's topological role and its development in the brain functional connectivity (FC) networks is essential for early disorder detection. A handful of previous studies have mostly focused on how FC network is changing regarding age. These approaches inevitably overlook the effect of individual variability for those at the same age that could shape unique cognitive capabilities and personalities among infants. With that in mind, we propose a novel computational framework based on across-subject across-age multilayer network analysis with a fully automatic (for parameter optimization), robust community detection algorithm. By detecting group consistent modules without losing individual information, this method allows a first-ever dissociation analysis of the two variability sources - age dependency and individual specificity - that greatly shape early brain development. This method is applied to a large cohort of 0-2 years old infants' functional MRI data during natural sleep. We not only detected the brain regions with greatest flexibility in this early developmental period but also identified five categories of brain regions with distinct development-related and individually variable flexibility changes. Our method is highly valuable for more thorough understanding of the early brain functional organizations and sheds light on early developmental abnormality detection.

Indexed as

Brain networkEarly developmentIndividual variability

Identifiers

PMID34337613
PMCPMC8320665
OpenAlexW3091348420

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.