Evidence map›Paper›PMID 39355441›Full record

ArticleNetwork neuroscience (Cambridge, Mass.)2024

A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence.

Aiying Zhang, Gemeng Zhang, Biao Cai, Tony W Wilson, Julia M Stephen, Vince D Calhoun, Yu-Ping Wang

Abstract read
In one paragraph

Article in Network neuroscience (Cambridge, Mass.), 2024. 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
–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

2 citing papers in PubMed.

  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

7 authors.

Aiying ZhangSchool of Data Science, University of Virginia, Charlottesville, VA, USA.ORCID https://orcid.org/0000-0001-9623-3922
Gemeng ZhangDepartment of Biomedical Engineering, Tulane University, New Orleans, LA, USA.
Biao CaiDepartment of Biomedical Engineering, Tulane University, New Orleans, LA, USA.
Tony W WilsonInstitute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA.
Julia M StephenMind Research Network, Albuquerque, NM, USA.
Vince D CalhounTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
Yu-Ping WangDepartment of Biomedical Engineering, Tulane University, New Orleans, LA, USA.

Funding

Stress-Induced Aberrations in the Mitochondrial Redox Environment Impact Developing Neural Circuits Supporting Cognitive ControlP20GM144641 · NIGMS · FATHER FLANAGAN'S BOYS' HOME · PI Giorgia Picci · 2022 to 2026
$15.1M
DEVELOPMENTAL MULTIMODAL IMAGING OF NEUROCOGNITIVE DYNAMICS (DEV-MIND)R01MH121101 · NIMH · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI WILSON, TONY W · 2019 to 2023
$5.8M
Unified multivariate data-driven solutions for static and dynamic brain connectivityR01EB020407 · NIBIB · THE MIND RESEARCH NETWORK · PI ADALI, TULAY, CALHOUN, VINCE D · 2015 to 2018
$2.7M
Neurophysiological markers of HAND and the impact of aging: Evidence from MEGR01MH103220 · NIMH · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI WILSON, TONY W · 2014 to 2018
$2.5M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
Integration of fMRI imaging, genomics, network and biological knowledgeR01MH107354 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2015 to 2018
$1.9M
Integration of multiscale genomic data for comprehensive analysis of complex diseR01GM109068 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2014 to 2018
$1.6M
Integration of brain imaging and multi-omics data for improved diagnosis and prediction of mental disordersR56MH124925 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2021 to 2022
$1.1M
NIBIB NIH HHS R01 EB020407NIGMS NIH HHS P20 GM144641NIGMS NIH HHS R01 GM109068NIMH NIH HHS R01 MH103220NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH107354NIMH NIH HHS R01 MH121101NIMH NIH HHS R56 MH124925
6 · The paper itself

Abstract

Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. Emotion identification skills emerge in infancy and continue to develop throughout childhood and adolescence. Understanding the development of the brain's emotion circuitry may help us explain the emotional changes during adolescence. In this work, we aim to deepen our understanding of emotion-related functional connectivity (FC) from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated association model into the estimation pipeline. Simulation results indicated stable and accurate performance over various settings, especially when the sample size was small. We used fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) to validate the approach. It included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and intermodular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information, respectively. In addition, several unique developmental hub structures and group-specific patterns were discovered. Our findings help provide a directed FC template of brain network organization underlying emotion processing during adolescence.

Indexed as

AdolescenceBayesian networkBrain developmentEmotion processingfMRI

Identifiers

PMID39355441
PMCPMC11349030

What OpenQuestion holds

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LicenceCC BY
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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.