Share：
Share this content in WeChat
X
[Chinese] [PDF] 29 1
• Clinical Article •
Independent component-based dynamic functional connectivity network analysis in manganese-exposed welders
RU Xuying  WU Jiayu  FAN Sijia  SUN Mengxue  CAO Yixin  GAO Ming  WU Xiaoping 

Cite this article as: RU X Y, WU J Y, FAN S J, et al. Independent component-based dynamic functional connectivity network analysis in manganese-exposed welders[J]. Chin J Magn Reson Imaging, 2026, 17(4): 41-46, 61. DOI:10.12015/issn.1674-8034.2026.04.006.


[Abstract] Objective To investigate alterations in dynamic functional connectivity network (dFNC) and its temporal properties among welders with occupational manganese (Mn) exposure.Materials and Methods We conducted a study on 25 Mn-exposed welders and 29 healthy control group matched with welders. Both groups underwent resting-state functional magnetic resonance imaging (rs-fMRI) scan on a 3.0 T MRI scanner. Following standard preprocessing, group independent component analysis (ICA) was performed to identify resting-state networks. dFNC was computed using a sliding window correlation approach, and the resulting connectivity matrices were clustered into recurring brain states via the K-means algorithm. Group differences in state-specific dFNC patterns and temporal metrics were then evaluated. Furthermore, correlations between aberrant dFNC features and clinical indicators were examined in the welder group.Results Compared with HCs, Mn-exposed welders showed significantly reduced dynamic functional connectivity between the salience network (SN) - executive control network (ECN), the default mode network (DMN) - language network (LAN), and the SN-DMN (t = -3.18、-3.31、-3.11, false discovery rate correction, P < 0.05). However, no significant between-group differences were found in other dFNC features (P > 0.05).Conclusions The dFNC between the SN, ECN and DMN of Mn-exposed welders were significantly reduced, which provided evidence value for understanding the neuropathological mechanisms related to Mn overexposure and early prevention of Mn poisoning.
[Keywords] manganese exposure；magnetic resonance imaging；independent component analysis；dynamic functional connectivity；brain network

RU Xuying1, 2   WU Jiayu2   FAN Sijia1, 2   SUN Mengxue1, 2   CAO Yixin1, 2   GAO Ming2   WU Xiaoping2*  

1 Yan'an University, Yan'an 716000, China

2 Department of Radiology, Xi'an Central Hospital Affiliated to Xi'an Jiaotong University, Xi'an 710003, China

Corresponding author: WU X P, E-mail: szping518@163.com

Conflicts of interest   None.

Received  2025-10-27
Accepted  2026-03-24
DOI: 10.12015/issn.1674-8034.2026.04.006
Cite this article as: RU X Y, WU J Y, FAN S J, et al. Independent component-based dynamic functional connectivity network analysis in manganese-exposed welders[J]. Chin J Magn Reson Imaging, 2026, 17(4): 41-46, 61. DOI:10.12015/issn.1674-8034.2026.04.006.

[1]
GANDHI D, RUDRASHETTI A P, RAJASEKARAN S. The impact of environmental and occupational exposures of manganese on pulmonary, hepatic, and renal functions[J]. J Appl Toxicol, 2022, 42(1): 103-129. DOI: 10.1002/jat.4214.
[2]
WU Y J, ZHOU W, LUO S H, et al. Research progress on multi-system health damage of worker by occupational manganese exposure[J]. Occup and Health, 2024, 40(6): 847-850. DOI: 10.13329/j.cnki.zyyjk.2024.0164.
[3]
BI Y J, CHEN H Q, LI X B, et al. Multiomics Research Progresson Neurotoxicity Inducedby Manganese Metal Exposure[J]. Chinese Journal of Inorganic Analytical Chemistry, 2024, 14(1): 63-71. DOI: 10.3969/j.issn.2095-1035.2024.01.008.
[4]
WEN Y, QI Z P, LI J S, et al. Suppression of axonal attractant netrin-1 injured dopaminergic neuronal and motor function of mice during manganese overexposure[J/OL]. Metallomics, 2022, 14(6): mfac091 [2025-10-27]. https://pubmed.ncbi.nlm.nih.gov/35383848/. DOI: 10.1093/mtomcs/mfac019.
[5]
UU H Z, PENG X R, LIU Y R, et al. Sleep Quality Modulates the Association between Dynamic Functional Network Connectivity and Cognitive Function in Healthy Older Adults[J]. Neuroscience, 2022, 480: 131-142. DOI: 10.1016/j.neuroscience.2021.11.018.
[6]
RECHTMAN E, NAVARRO E, WATER E D, et al. Early-Life Critical Windows of Susceptibility to Manganese Exposure and Sex-Specific Changes in Brain Connectivity in Late Adolescence[J]. Biol Psychiatry Glob Open Sci, 2023, 3(3): 460-469. DOI: 10.1016/j.bpsgos.2022.03.016.
[7]
QIAO L, ZHANG H, KIM M, et al. Estimating functional brain networks by incorporating a modularity prior[J]. Neuroimage, 2016, 141: 399-407. DOI: 10.1016/j.neuroimage.2016.07.058.
[8]
ZHANG H, CHEN X B, SHI F, et al. Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment[J]. J Alzheimers Dis, 2016, 54(3): 1095-1112. DOI: 10.3233/jad-160092.
[9]
ZOU Y R, YU T, ZHU L W, et al. Altered dynamic functional connectivity of nucleus accumbens subregions in major depressive disorder: the interactive effect of childhood trauma and diagnosis[J/OL]. Soc Cogn Affect Neurosci, 2024, 19(1): nsae053 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11389612/. DOI: 10.1093/scan/nsae053.
[10]
MALTBIE E, YOUSEFI B, ZHANG X, et al. Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics[J/OL]. Front Neural Circuits, 2022, 16: 681544 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9013751/. DOI: 10.3389/fncir.2022.681544.
[11]
SU D N, JI L X, CUI Y S, et al. Altered Visuomotor Network Dynamics Associated with Freezing of Gait in Parkinson's Disease[J]. Mov Disord, 2025, 40(6): 1112-1122. DOI: 10.1002/mds.30146.
[12]
JIANG F, JIN H, GAO Y, et al. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging[J/OL]. Neuroimage, 2022, 254: 119131 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9942947/. DOI: 10.1016/j.neuroimage.2022.119131.
[13]
ALLEN E A, DAMARAJU E, PLIS S M, et al. Tracking whole-brain connectivity dynamics in the resting state[J]. Cereb Cortex, 2014, 24(3): 663-676. DOI: 10.1093/cercor/bhs352.
[14]
ESPINOZA F A, LIU J, CIAROCHI J, et al. Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures[J]. Hum Brain Mapp, 2019, 40(6): 1955-1968. DOI: 10.1002/hbm.24504.
[15]
QIN L, ZHOU Q, SUN Y T, et al. Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models[J/OL]. J Transl Med, 2024, 22(1): 763 [2025-08-20]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11323657/. DOI: 10.1186/s12967-024-05580-2.
[16]
LYU W J, WU Y, HUANG H M, et al. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals[J]. Cogn Neurodyn, 2023, 17(6): 1525-1539. DOI: 10.1007/s11571-022-09899-8.
[17]
WANG Y Y, WANG C H, MIAO P F, et al. An imbalance between functional segregation and integration in patients with pontine stroke: A dynamic functional network connectivity study[J/OL]. Neuroimage Clin, 2020, 28: 102507 [2025-08-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC7714678/. DOI: 10.1016/j.nicl.2020.102507.
[18]
EAVANI H, SATTERTHWAITE T D, FILIPOVYCH R, et al. Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI[J]. Neuroimage, 2015, 105: 286-299. DOI: 10.1016/j.neuroimage.2014.09.058.
[19]
FU L, ZHANG W, BI Y, et al. Altered Dynamics of Brain Spontaneous Activity and Functional Networks Associated With Cognitive Impairment in Patients With Type 2 Diabetes[J]. J Magn Reson Imaging, 2024, 60(6): 2547-2561. DOI: 10.1002/jmri.29306.
[20]
ALLEN E A, DAMARAJU E, EICHELE T, et al. EEG Signatures of Dynamic Functional Network Connectivity States[J]. Brain Topogr, 2018, 31(1): 101-116. DOI: 10.1007/s10548-017-0546-2.
[21]
ADAMOVICH T, ISMATULLINA V, CHIPEEVA N, et al. Task-specific topology of brain networks supporting working memory and inhibition[J/OL]. Hum Brain Mapp, 2024, 45(13): e70024 [2025-08-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11387957/. DOI: 10.1002/hbm.70024.
[22]
FIORENZATO E, STRAFELLA A P, KIM J, et al. Dynamic functional connectivity changes associated with dementia in Parkinson's disease[J]. Brain, 2019, 142(9): 2860-2872. DOI: 10.1093/brain/awz192.
[23]
LUO L, LI Q, YOU W, et al. Altered brain functional network dynamics in obsessive-compulsive disorder[J]. Hum Brain Mapp, 2021, 42(7): 2061-2076. DOI: 10.1002/hbm.25345.
[24]
ROLLS E T. The cingulate cortex and limbic systems for emotion, action, and memory[J]. Brain Struct Funct, 2019, 224(9): 3001-3018. DOI: 10.1007/s00429-019-01945-2.
[25]
TEGHIL A, BONAVITA A, GUARIGLIA C, et al. Commonalities and specificities between environmental navigation and autobiographical memory: A synthesis and a theoretical perspective[J]. Neurosci Biobehav Rev, 2021, 127: 928-945. DOI: 10.1016/j.neubiorev.2021.06.012.
[26]
LIANG X, ZOU Q H, HE Y, et al. Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads[J]. Cereb Cortex, 2016, 26(4): 1501-1511. DOI: 10.1093/cercor/bhu316.
[27]
JIANG G H, FENG Y, LI M, et al. Distinct alterations of functional connectivity of the basal forebrain subregions in insomnia disorder[J/OL]. Front Psychiatry, 2022, 13: 1036997 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9606586/. DOI: 10.3389/fpsyt.2022.1036997.
[28]
SMALLWOOD J, BERNHARDT B C, LEECH R, et al. The default mode network in cognition: a topographical perspective[J]. Nat Rev Neurosci, 2021, 22(8): 503-513. DOI: 10.1038/s41583-021-00474-4.
[29]
XIAO Z D, CHEN B, YANG M F, et al. Abnormal functional network connectivity mediates the relationship between depressive symptoms and cognitive decline in late-onset depression[J/OL]. Psychol Med, 2025, 55: e227 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC12551583/. DOI: 10.1017/s0033291725100706.
[30]
SONG Y, XU W W, CHEN S S, et al. Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis[J/OL]. Front Aging Neurosci, 2021, 13: 695210 [2025-08-24]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8350339/. DOI: 10.3389/fnagi.2021.695210.
[31]
VON GAL A, PAPA D, D'AURIA M, et al. Disruptive resting state networks characterizing depressive comorbidity in Alzheimer's disease and mild cognitive impairment[J]. J Alzheimers Dis, 2025, 106(1): 18-37. DOI: 10.1177/13872877251337770.
[32]
CHEN Q, CHEN X G, HE X X, et al. Aberrant structural and functional connectivity in the salience network and central executive network circuit in schizophrenia[J]. Neurosci Lett, 2016, 627: 178-184. DOI: 10.1016/j.neulet.2016.05.035.
[33]
PARLAR M, DENSMORE M, HALL G B, et al. Relation between patterns of intrinsic network connectivity, cognitive functioning, and symptom presentation in trauma-exposed patients with major depressive disorder[J/OL]. Brain Behav, 2017, 7(5): e00664 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC5434180/. DOI: 10.1002/brb3.664.
[34]
JIA J N, YAN C Q, QI X H, et al. Effect of acupuncture on default mode network in patients with migraine based on functional Magnetic Resonance Imaging: a preliminary study[J]. Chinese Acupuncture & Moxibustion, 2021, 41(10): 1074-1078. DOI: 10.13703/j.0255-2930.20201002-k0001.
[35]
SHEN B, YAO Q, ZHANG Y, et al. Static and Dynamic Functional Network Connectivity in Parkinson's Disease Patients With Postural Instability and Gait Disorder[J/OL]. CNS Neuroscience & Therapeutics, 2024, 30(11): e70115 [2025-10-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11551039/. DOI: 10.1111/cns.70115.
[36]
ZHANG D S, LEI Y M, GAO J, et al. Right Frontoinsular Cortex: A Potential Imaging Biomarker to Evaluate T2DM-Induced Cognitive Impairment[J/OL]. Front Aging Neurosci, 2021, 13: 674288 [2025-10-27]. http://pmc.ncbi.nlm.nih.gov/articles/PMC8193040/. DOI: 10.3389/fnagi.2021.674288.
[37]
ZHAO C, HUANG W J, FENG F, et al. Abnormal characterization of dynamic functional connectivity in Alzheimer's disease[J]. Neural Regen Res, 2022, 17(9): 2014-2021. DOI: 10.4103/1673-5374.332161.
[38]
ASCHNER M, ERIKSON K M, DORMAN D C. Manganese dosimetry: species differences and implications for neurotoxicity[J]. Crit Rev Toxicol, 2005, 35(1): 1-32. DOI: 10.1080/10408440590905920.
[39]
TJALVE H, HENRIKSSON J. Uptake of metals in the brain via olfactory pathways[J]. Neurotoxicology, 1999, 20(2-3): 181-195.

PREV Study on chemotherapy related cognitive disorders and brain function abnormalities in breast cancer patients based on functional connections of anterior cingulate gyrus
NEXT Causal structural covariance networks reveal mechanisms of gray matter atrophy in Alzheimer<sup><sup>,</sup></sup>s disease
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn