Evidence map›Paper›PMID 34170056›Full record

ArticleHuman brain mapping2021

Age-related and individual variations in altered prefrontal and cerebellar connectivity associated with the tendency of developing internet addiction.

Abhishek Uday Patil, Deepa Madathil, Chih-Mao Huang

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
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  6. Connectome-based predictive modeling of Internet addiction symptomatology.Social cognitive and affective neuroscience · 2024
    Article
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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

3 authors at 2 institutions in 2 countries.

Abhishek Uday PatilDepartment of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.ORCID 0000-0001-7577-7875
Deepa MadathilDepartment of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.ORCID 0000-0002-9103-901X
Chih-Mao HuangDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.ORCID 0000-0002-6209-2575
National Yang Ming Chiao Tung University · TWVellore Institute of Technology University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Internet addiction refers to problematic patterns of internet use that continually alter the neural organization and brain networks that control impulsive behaviors and inhibitory functions. Individuals with elevated tendencies to develop internet addiction represent the transition between healthy and clinical conditions and may progress to behavioral addictive disorders. In this network neuroscience study, we used resting-state functional magnetic resonance imaging (rs-fMRI) to examine how and whether individual variations in the tendency of developing internet addiction rewire functional connectivity and diminish the amplitude of spontaneous low-frequency fluctuations in healthy brains. The influence of neurocognitive aging (aged over 60 years) on executive-cerebellar networks responsible for internet addictive behavior was also investigated. Our results revealed that individuals with an elevated tendency of developing internet addiction had disrupted executive-cerebellar networks but increased occipital-putamen connectivity, probably resulting from addiction-sensitive cognitive control processes and bottom-up sensory plasticity. Neurocognitive aging alleviated the effects of reduced mechanisms of prefrontal and cerebellar connectivity, suggesting age-related modulation of addiction-associated brain networks in response to compulsive internet use. Our findings highlight age-related and individual differences in altered functional connectivity and the brain networks of individuals at a high risk of developing internet addictive disorders. These results offer novel network-based preclinical markers of internet addictive behaviors for individuals of different ages.

Indexed as

Biological Variation, IndividualConnectomeIndividualityAdolescentAdultAgedBrainCognitive AgingFemaleHumansInternet Addiction DisorderMaleMiddle AgedYoung AdultagingALFFfunctional connectivityinternet addiction tendency

Identifiers

PMID34170056
PMCPMC8410527
OpenAlexW3174080611

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.