Share:
Share this content in WeChat
X
Clinical Article
A study on rs-fMRI dynamic functional network connectivity in patients with type 2 diabetic
MEI Leilei  YANG Hongkai  ZHANG Manman  SHEN Xinru  XU Qi  HE Yongsheng 

Cite this article as: MEI L L, YANG H K, ZHANG M M, et al. A study on rs-fMRI dynamic functional network connectivity in patients with type 2 diabetic[J]. Chin J Magn Reson Imaging, 2024, 15(1): 82-87. DOI:10.12015/issn.1674-8034.2024.01.013.


[Abstract] Objective To investigate the temporal properties of brain functional connectivity in patients with type 2 diabetes mellitus (T2DM) and the correlation between dynamic functional connectivity and clinical parameters by using dynamic functional network connectivity (dFNC) analysis.Materials and Methods The clinical and imaging data of 31 patients with T2DM were prospectively collected, and the diabetes-related biochemical indicators and neuropsychological test scores were recorded. At the same time, 32 healthy controls (HC) matched with age, gender and education level were recruited. Four functional connectivity states and three dFNC indexes (mean dwell time, fraction time, number of transitions) were obtained by using sliding time window technology. Two independent samples t-test was used to calculate the differences of FNC matrix and dFNC indexes between groups in different states. Spearman correlation analysis was used to calculate the correlation between dFNC indexes and clinical data in T2DM group.Results In state 1 weak connection, compared with HC group, T2DM group had longer mean dwell time (t=2.086, P<0.05). In state 3 local strong connection, compared with HC group, T2DM group had shorter mean dwell time (t=-2.250, P<0.05) and smaller fraction time (t=-2.582, P<0.05) ; the functional connectivity between default mode network (DMN) and visual network (VIS) was decreased (t=-4.875, P<0.05, FDR corrected). The duration of T2DM was positively correlated with the mean dwell time of state 1 weak connection (r=0.42, P<0.05), while other diabetes-related biochemical indexes and cognitive function scores were not correlated with dFNC indexes (P>0.05).Conclusions The dFNC analysis can capture more potential information about the changes of brain network connectivity in T2DM patients, and reveal the complex temporal characteristics and activity patterns of brain networks, which is expected to provide a new perspective for the neurobiological mechanisms of T2DM related cognitive impairment.
[Keywords] type 2 diabetes mellitus;cognitive impairment;magnetic resonance imaging;functional magnetic resonance imaging;dynamic functional network connectivity

MEI Leilei1   YANG Hongkai1   ZHANG Manman1   SHEN Xinru2   XU Qi1   HE Yongsheng1*  

1 Department of Radiology, Maanshan People's Hospital, Maanshan 24300, China

2 Department of Endocrinology, Maanshan People's Hospital, Maanshan 24300, China

Corresponding author: HE Y S, E-mail: heyongsheng881@163.com

Conflicts of interest   None.

Received  2023-07-22
Accepted  2024-01-04
DOI: 10.12015/issn.1674-8034.2024.01.013
Cite this article as: MEI L L, YANG H K, ZHANG M M, et al. A study on rs-fMRI dynamic functional network connectivity in patients with type 2 diabetic[J]. Chin J Magn Reson Imaging, 2024, 15(1): 82-87. DOI:10.12015/issn.1674-8034.2024.01.013.

[1]
SAEEDI P, PETERSOHN I, SALPEA P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition[J/OL]. Diabetes Res Clin Pract, 2019, 157: 107843 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/31518657/. DOI: 10.1016/j.diabres.2019.107843.
[2]
MANSCHOT S M, BRANDS A M, VAN DER GROND J, et al. Brain magnetic resonance imaging correlates of impaired cognition in patients with type 2 diabetes[J]. Diabetes, 2006, 55(4): 1106-1113. DOI: 10.2337/diabetes.55.04.06.db05-1323.
[3]
MEI L L, ZHANG M M, YANG H K, et al. Research progress on neuroimaging biomarkers of cognitive impairment in patients with type 2 diabetes[J]. Chin J Magn Reson Imaging, 2023, 14(9): 108-113. DOI: 10.12015/issn.1674-8034.2023.09.020.
[4]
MENG J, LIU J, LI H, et al. Impairments in intrinsic functional networks in type 2 diabetes: A meta-analysis of resting-state functional connectivity[J/OL]. Front Neuroendocrinol, 2022, 66: 100992 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/35278579/. DOI: 10.1016/j.yfrne.2022.100992.
[5]
LEI Y, ZHANG D, QI F, et al. Dysfunctional interaction between the dorsal attention network and the default mode network in patients with type 2 diabetes mellitus[J/OL]. Front Hum Neurosci, 2021, 15: 796386 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/35002661/. DOI: 10.3389/fnhum.2021.796386.
[6]
PRETI M G, BOLTON T A, VAN DE VILLE D. The dynamic functional connectome: State-of-the-art and perspectives[J]. Neuroimage, 2017, 160: 41-54. DOI: 10.1016/j.neuroimage.2016.12.061.
[7]
WANG Y, WANG C, MIAO P, 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 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/33395996/. DOI: 10.1016/j.nicl.2020.102507.
[8]
YANG W, XU X, WANG C, et al. Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia[J]. Brain Imaging Behav, 2022, 16(3): 1294-1302. DOI: 10.1007/s11682-021-00592-8.
[9]
ZHENG R, CHEN Y, JIANG Y, et al. Abnormal dynamic functional connectivity in first-episode, drug-naïve adolescents with major depressive disorder[J]. J Neurosci Res, 2022, 100(7): 1463-1475. DOI: 10.1002/jnr.25047.
[10]
SCHUMACHER J, PERAZA L R, FIRBANK M, et al. Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease[J/OL]. Neuroimage Clin, 2019, 22: 101812 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/30991620/. DOI: 10.1016/j.nicl.2019.101812.
[11]
LYU W, WU Y, HUANG H, et al. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals[J/OL]. Cognitive Neurodynamics, 2022 [2023-07-18]. https://link.springer.com/article/10.1007/s11571-022-09899-8#citeas. DOI: 10.1007/s11571-022-09899-8.
[12]
American Diabetes Association. Diagnosis and classification of diabetes mellitus[J]. Diabetes Care, 2014, 37(Suppl 1): S81-S90. DOI: 10.2337/dc14-S081.
[13]
VIVIANO R P, RAZ N, YUAN P, et al. Associations between dynamic functional connectivity and age, metabolic risk, and cognitive performance[J]. Neurobiol Aging, 2017, 59: 135-143. DOI: 10.1016/j.neurobiolaging.2017.08.003.
[14]
GU Y, LIN Y, HUANG L, et al. Abnormal dynamic functional connectivity in Alzheimer's disease[J]. CNS Neurosci Ther, 2020, 26(9): 962-971. DOI: 10.1111/cns.13387.
[15]
MARUSAK H A, CALHOUN V D, BROWN S, et al. Dynamic functional connectivity of neurocognitive networks in children[J]. Hum Brain Mapp, 2017, 38(1): 97-108. DOI: 10.1002/hbm.23346.
[16]
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.
[17]
FU Z, CAPRIHAN A, CHEN J, et al. Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities[J]. Hum Brain Mapp, 2019, 40(11): 3203-3221. DOI: 10.1002/hbm.24591.
[18]
XIONG Y, TIAN T, FAN Y, et al. Diffusion tensor imaging reveals altered topological efficiency of structural networks in type-2 diabetes patients with and without mild cognitive impairment[J]. J Magn Reson Imaging, 2022, 55(3): 917-927. DOI: 10.1002/jmri.27884.
[19]
FANG F, LAI M Y, HUANG J J, et al. Compensatory hippocampal connectivity in young adults with early-stage type 2 diabetes[J]. J Clin Endocrinol Metab, 2019, 104(7): 3025-3038. DOI: 10.1210/jc.2018-02319.
[20]
LI Y, LIANG Y, TAN X, et al. Altered functional hubs and connectivity in type 2 diabetes mellitus without mild cognitive impairment[J/OL]. Front Neurol, 2020, 11: 1016 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/33071928/. DOI: 10.3389/fneur.2020.01016.
[21]
WANG J, ZHOU S, DENG D, et al. Compensatory thalamocortical functional hyperconnectivity in type 2 Diabetes Mellitus[J]. Brain Imaging Behav, 2022, 16(6): 2556-2568. DOI: 10.1007/s11682-022-00710-0.
[22]
BONKHOFF A K, ESPINOZA F A, GAZULA H, et al. Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states[J]. Brain, 2020, 143(5): 1525-1540. DOI: 10.1093/brain/awaa101
[23]
LÓPEZ-VICENTE M, AGCAOGLU O, PÉREZ-CRESPO L, et al. Developmental changes in dynamic functional connectivity from childhood into adolescence[J/OL]. Front Syst Neurosci, 2021, 15: 724805 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/34880732/. DOI: 10.3389/fnsys.2021.724805.
[24]
GALLEN C L, D'ESPOSITO M. Brain Modularity: A biomarker of intervention-related plasticity[J]. Trends Cogn Sci, 2019, 23(4): 293-304. DOI: 10.1016/j.tics.2019.01.014.
[25]
GALLEN C L, BANIQUED P L, CHAPMAN S B, et al. Modular brain network organization predicts response to cognitive training in older adults[J/OL]. PLoS One, 2016, 11(12): e0169015 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/28006029/. DOI: 10.1371/journal.pone.0169015.
[26]
BASSETT D S, WYMBS N F, PORTER M A, et al. Dynamic reconfiguration of human brain networks during learning[J]. Proc Natl Acad Sci U S A, 2011, 108(18): 7641-7646. DOI: 10.1073/pnas.1018985108.
[27]
BRIER M R, THOMAS J B, FAGAN A M, et al. Functional connectivity and graph theory in preclinical Alzheimer's disease[J]. Neurobiol Aging, 2014, 35(4): 757-768. DOI: 10.1016/j.neurobiolaging.2013.10.081.
[28]
YANG G J, MURRAY J D, WANG X J, et al. Functional hierarchy underlies preferential connectivity disturbances in schizophrenia[J/OL]. Proc Natl Acad Sci U S A, 2016, 113(2): E219-E228 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/26699491/. DOI: 10.1073/pnas.1508436113.
[29]
SIEGEL J S, SEITZMAN B A, RAMSEY L E, et al. Re-emergence of modular brain networks in stroke recovery[J]. Cortex, 2018, 101: 44-59. DOI: 10.1016/j.cortex.2017.12.019.
[30]
FOX M D, SNYDER A Z, ZACKS J M, et al. Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses[J]. Nat Neurosci, 2006, 9(1): 23-25. DOI: 10.1038/nn1616.
[31]
ANDREWS-HANNA J R, REIDLER J S, HUANG C, et al. Evidence for the default network's role in spontaneous cognition[J]. J Neurophysiol, 2010, 104(1): 322-335. DOI: 10.1152/jn.00830.2009.
[32]
SMALLWOOD J, SCHOOLER J W. The restless mind[J]. Psychol Bull, 2006, 132(6): 946-958. DOI: 10.1037/0033-2909.132.6.946.
[33]
RAICHLE M E. The brain's default mode network[J]. Annu Rev Neurosci, 2015, 38: 433-447. DOI: 10.1146/annurev-neuro-071013-014030.
[34]
YESHURUN Y, NGUYEN M, HASSON U. The default mode network: where the idiosyncratic self meets the shared social world[J]. Nat Rev Neurosci, 2021, 22(3): 181-192. DOI: 10.1038/s41583-020-00420-w.
[35]
CHENG X, YUAN Y, WANG Y, et al. Neural antagonistic mechanism between default-mode and task-positive networks[J]. Neurocomputing, 2020, 417: 74-85. DOI: 10.1016/j.neucom.2020.07.079.
[36]
HINDRIKS R, Mantini R, Gravel N, et al. Latency analysis of resting-state BOLD-fMRI reveals traveling waves in visual cortex linking task-positive and task-negative networks[J]. NeuroImage, 2019, 200: 259-274. DOI: 10.1016/j.neuroimage.2019.06.007.
[37]
JING J, ZHOU Y, PAN Y, et al. Reduced white matter microstructural integrity in prediabetes and diabetes: A population-based study[J/OL]. EBioMedicine, 2022, 82: 104144 [2023-07-18]. https://pubmed.ncbi.nlm.nih.gov/35810560/. DOI: 10.1016/j.ebiom.2022.104144.
[38]
VAN BUSSEL F C, BACKES W H, VAN VEENENDAAL T M, et al. Functional brain networks are altered in type 2 diabetes and prediabetes: Signs for compensation of cognitive decrements? The maastricht study[J]. Diabetes, 2016, 65(8): 2404-2413. DOI: 10.2337/db16-0128.
[39]
Chinese Society of Endocrinology, the Blood Pressure Control Target in Diabetes (BPROAD) Research Group. Chinese expert consensus on the prevention and management of cognitive impairment in patients with type 2 diabetes mellitus[J]. Chin J Endocrinol Metab, 2022, 38(06): 453-464. DOI: 10.3760/cma.j.cn311282-20220518-00320.

PREV The correlation between microstructural changes in the hypothalamus and mild cognitive impairment and short-chain fatty acids in the gut in T2DM patients
NEXT Value of combining radiomics and deep-learning with hematological inflammatory markers in predicting the prognosis of glioma
  



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