Evidence map›Paper›PMID 39292711›Full record

ArticlePLoS biology2024

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

Tianyuan Lei, Xuhong Liao, Xinyuan Liang, Lianglong Sun, Mingrui Xia, Yunman Xia, Tengda Zhao, Xiaodan Chen, Weiwei Men, Yanpei Wang and 15 more

Abstract read
In one paragraph

Article in PLoS biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

25 authors.

Tianyuan LeiDepartment of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Xuhong LiaoSchool of Systems Science, Beijing Normal University, Beijing, China.
Xinyuan LiangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Lianglong SunState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Mingrui XiaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yunman XiaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Tengda ZhaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Xiaodan ChenState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Weiwei MenCenter for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Yanpei WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Leilei MaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Ningyu LiuState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Jing LuState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Gai ZhaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yuyin DingState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yao DengState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Jiali WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Rui ChenState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Haibo ZhangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Shuping TanPsychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical College, Beijing, China.
Jia-Hong GaoCenter for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Shaozheng QinState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Sha TaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Qi DongState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yong HeState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.ORCID 0000-0002-7039-2850

Funding

Beijing Brain Initiative of the Beijing Municipal Science & Technology Commission Z181100001518003National Key R&D Program of China 2018YFA0701402National Natural Science Foundation of China 82021004
6 · The paper itself

Abstract

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Indexed as

BrainConnectomeNerve NetAdolescentChildDiffusion Magnetic Resonance ImagingFemaleGray MatterHumansMagnetic Resonance ImagingMaleWhite Matter

Identifiers

PMID39292711
PMCPMC11441662

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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.