Evidence map›Paper›PMID 36214186›Full record

ArticleHuman brain mapping2023

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing.

Tiantian Liu, Zhongyan Shi, Jian Zhang, Kexin Wang, Yuanhao Li, Guangying Pei, Li Wang, Jinglong Wu, Tianyi Yan

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  3. 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

9 authors at 1 institution in 1 country.

Tiantian LiuSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Zhongyan ShiSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Jian ZhangIntelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Kexin WangSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Yuanhao LiSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Guangying PeiSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Li WangSchool of Life Science, Beijing Institute of Technology, Beijing, China.ORCID 0000-0001-9819-9051
Jinglong WuSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Tianyi YanSchool of Life Science, Beijing Institute of Technology, Beijing, China.ORCID 0000-0002-2674-4134
Beijing Institute of Technology · CN

Funding

Biotechnology and Biological Sciences Research Council BB/H008217/1Medical Research Council
6 · The paper itself

Abstract

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

Indexed as

Brain MappingHealthy AgingBrainHumansMagnetic Resonance ImagingNeural Pathwaysbrain statesclusteringdynamic functional connectivityindividual sliding windowresting-state fMRI

Identifiers

PMID36214186
PMCPMC9842897
OpenAlexW4303986041

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.