Evidence map›Paper›PMID 42415272›Full record

ArticleHuman brain mapping2026

Diurnal Variations and Test-Retest Reliability of Resting-State Functional MRI Metrics.

Bowen Guo, Kaikai Yan, Zhihui Liu, Yao Deng, Shanna Fu, Weiwei Zhao, Jing Xu, Caihong Jiang, Ruiwen Tao, Xing Chen and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human brain mapping, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Bowen GuoDepartment of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, China.ORCID https://orcid.org/0009-0002-0015-9284
Kaikai YanCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Zhihui LiuCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Yao DengState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Shanna FuCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Weiwei ZhaoCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.ORCID https://orcid.org/0000-0001-8507-1921
Jing XuCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Caihong JiangCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Ruiwen TaoDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Xing ChenCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Mengmeng WangBusiness School, NingboTech University, Ningbo, China.
Tianxin MaoCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.ORCID https://orcid.org/0000-0002-7038-0358
Hengyi RaoCenter for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.ORCID https://orcid.org/0000-0003-2735-2500

Funding

Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project 2021ZD0200500Major Humanities and Social Research Projects in Zhejiang higher education institutions 2024QN071National Natural Science Foundation of China 32200889National Natural Science Foundation of China 32441108Open Research Fund of the State Key Laboratory of Cognitive Science and Mental HealthShanghai International Studies University Research Projects 2021114002Zhejiang Provincial Natural Science Foundation of China LQN25G020003
6 · The paper itself

Abstract

Resting-state fMRI (rs-fMRI) is widely used to assess intrinsic brain activity, yet concerns about its test-retest reliability and reproducibility persist. Circadian rhythms strongly influence brain physiology, but their impact on rs-fMRI reliability remains poorly understood. In this study, we scanned 39 healthy young adults six times within a single day (08:00-20:00) under standardized conditions. For each session, we computed four common rs-fMRI metrics, including amplitude of low-frequency fluctuations (ALFF), wavelet-transformed ALFF (wALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo), and assessed reliability using intraclass correlation coefficients (ICCs). ReHo showed relatively higher and more stable reliability across sessions, whereas amplitude-based metrics, particularly fALFF, exhibited greater diurnal variation. Both network-level and region-specific analyses revealed low reliability in the limbic and subcortical structures, with a mid-morning dip at 10:00. Moreover, ICCs for ALFF, wALFF, and fALFF declined with increasing inter-scan intervals, whereas ReHo remained robust. These findings demonstrate diurnal fluctuations in rs-fMRI reliability, with different metrics exhibiting distinct temporal stability profiles. We recommend that scan timing and circadian influences should be explicitly considered in the design, analysis, and interpretation of future rs-fMRI studies.

Indexed as

BrainCircadian RhythmMagnetic Resonance ImagingAdultBrain MappingFemaleHumansImage Processing, Computer-AssistedMaleReproducibility of ResultsRestYoung Adultdiurnal variationintraclass correlation coefficient (ICC)resting‐state fMRItest–retest reliabilitytime‐of‐day

Identifiers

PMID42415272
PMCPMC13341652

What Socratic 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 Socratic graph.