Share：
Share this content in WeChat
X
[Chinese] [PDF] 19 1
• Clinical Article •
Construction of cerebral blood flow networks based on Jensen-Shannon divergence: Graph theoretical analysis in patients with mild cognitive impairment
LIANG Yanni  YANG Dan  YANG Wenxia  KANG Siru  LI Yisu  ZHANG Jing 

Cite this article as: LIANG Y N, YANG D, YANG W X, et al. Construction of cerebral blood flow networks based on Jensen-Shannon divergence: Graph theoretical analysis in patients with mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2026, 17(9): 92-101, 109. DOI:10.12015/issn.1674-8034.2026.09.013.


[Abstract] Objective To construct cerebral blood flow functional networks using arterial spin labeling (ASL), investigate their topological characteristics and associations with cognitive function in patients with mild cognitive impairment (MCI), and evaluate the potential value of related network metrics for the early diagnosis of MCI.Materials and Methods A total of 113 patients with MCI and 83 age- and sex-matched healthy controls were prospectively enrolled. Cerebral blood flow (CBF) was quantitatively measured using ASL. The brain was parcellated into 116 regions according to the Automated Anatomical Labeling (AAL) atlas, with these regions defined as network nodes. Kernel density estimation was subsequently used to model the probability distribution of CBF values within each brain region. Jensen-Shannon divergence (JSD) was applied to quantify differences between regional CBF probability distributions, and individual cerebral blood flow networks were constructed through similarity transformation. Global and nodal topological metrics were calculated using graph-theoretical analysis, and the area under the curve (AUC) of each metric across a sparsity range of 0.04 to 0.40 was used for statistical analysis. Between-group comparisons were performed using a nonparametric permutation test based on covariate residualization with 10,000 permutations, controlling for age, sex, and years of education. Multiple comparisons were corrected using the Benjamini-Hochberg false discovery rate (FDR) method. Partial Spearman correlation analyses controlling for age, sex, and years of education were conducted to evaluate the associations between network metrics and scores on the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Digit Span Test (DST).Results Compared with the healthy control group, the MCI group exhibited cerebral blood flow network alterations predominantly at the nodal level. (1) Global properties: Global efficiency and the small-world coefficient tended to increase, whereas path length tended to decrease in patients with MCI; however, none of these differences remained statistically significant after FDR correction. (2) Nodal properties: Within core regions of the default mode network, nodal local efficiency and clustering coefficient were decreased in the left posterior cingulate gyrus and left inferior parietal lobule, suggesting impaired local information processing. Increased betweenness centrality in the left inferior frontal gyrus suggests an enhanced bridging role in network information transfer. The parahippocampal gyrus showed asymmetric alterations between the hemispheres, with increased nodal local efficiency on the left and increased nodal degree centrality and nodal efficiency on the right. Nodal efficiency and clustering coefficient were also increased in basal ganglia regions, including the caudate nucleus and lentiform nucleus (FDR-corrected P < 0.05). (3) Correlation analysis: MMSE scores were positively correlated with the clustering coefficient and nodal local efficiency of the left triangular part of the inferior frontal gyrus, whereas DST scores were positively correlated with normalized characteristic path length. MMSE scores were negatively correlated with global efficiency, normalized clustering coefficient, small-world coefficient, and betweenness centrality of the left triangular part of the inferior frontal gyrus (FDR-corrected P < 0.05).Conclusions Cerebral blood flow network abnormalities in patients with MCI were primarily characterized by nodal topological reorganization in key brain regions. Several network metrics showed statistically significant associations with cognitive scores, suggesting that these metrics may reflect individual differences in cognitive function in patients with MCI and may have potential value for adjunctive cognitive assessment.
[Keywords] mild cognitive impairment；magnetic resonance imaging；brain network；cerebral blood flow；arterial spin labeling；Graph theoretic analysis

LIANG Yanni1, 2, 3   YANG Dan1, 2, 3   YANG Wenxia1, 2, 3   KANG Siru1, 2, 3   LI Yisu1, 2, 3   ZHANG Jing1, 2, 3*  

1 Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Second Clinical School, Lanzhou University, Lanzhou 730030, China

3 Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China

Corresponding author: ZHANG J, E-mail: ery_zhangjing@lzu.edu.cn

Conflicts of interest   None.

Received  2026-07-04
Accepted  2026-08-22
DOI: 10.12015/issn.1674-8034.2026.09.013
Cite this article as: LIANG Y N, YANG D, YANG W X, et al. Construction of cerebral blood flow networks based on Jensen-Shannon divergence: Graph theoretical analysis in patients with mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2026, 17(9): 92-101, 109. DOI:10.12015/issn.1674-8034.2026.09.013.

[1]
PETERSEN R C. Mild cognitive impairment[J]. Continuum, 2016, 22(2): 404-418. DOI: 10.1212/con.0000000000000313.
[2]
JIA L F, DU Y F, CHU L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study[J/OL]. Lancet Public Health, 2020, 5(12): e661-e671 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/33271079/. DOI: 10.1016/S2468-2667(20)30185-7.
[3]
ALBERT M S, DEKOSKY S T, DICKSON D, et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease[J]. Alzheimers Dement, 2011, 7(3): 270-279. DOI: 10.1016/j.jalz.2011.03.008.
[4]
XUE C, ZHENG D R, RUAN Y M, et al. Reorganized brain functional network topology in stable and progressive mild cognitive impairment[J/OL]. Front Aging Neurosci, 2024, 16: 1467054 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/39624168/. DOI: 10.3389/fnagi.2024.1467054.
[5]
HE W J, XIE Q, WANG Y J, et al. Modularity segregation of the brain functional networks in mild cognitive impairment[J]. Chin J Med Imag, 2023, 31(8): 799-802, 812. DOI: 10.3969/j.issn.1005-5185.2023.08.001.
[6]
ZHONG J W, LI G, LV Z X, et al. Neuromodulation of cerebral blood flow: a physiological mechanism and methodological review of neurovascular coupling[J/OL]. Bioengineering, 2025, 12(5): 442 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/40428061/. DOI: 10.3390/bioengineering12050442.
[7]
BAMBACH S, SMITH M, MORRIS P P, et al. Arterial spin labeling applications in pediatric and adult neurologic disorders[J]. J Magn Reson Imag, 2022, 55(3): 698-719. DOI: 10.1002/jmri.27438.
[8]
LI M, ZHU T J, KANG Y, et al. A 3D pseudo-continuous arterial spin labeling study of altered cerebral blood flow correlation networks in mild cognitive impairment and Alzheimer's disease[J/OL]. Front Aging Neurosci, 2024, 16: 1345251 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/38721017/. DOI: 10.3389/fnagi.2024.1345251.
[9]
SÁNCHEZ-CATASÚS C A, WILLEMSEN A, BOELLAARD R, et al. Episodic memory in mild cognitive impairment inversely correlates with the global modularity of the cerebral blood flow network[J/OL]. Psychiatry Res Neuroimaging, 2018, 282: 73-81 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/30419408/. DOI: 10.1016/j.pscychresns.2018.11.003.
[10]
WU C W, HE Y, LI J L, et al. A novel method for functional brain networks based on static cerebral blood flow[J/OL]. Neuroimage, 2025, 308: 121069 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/39889811/. DOI: 10.1016/j.neuroimage.2025.121069.
[11]
FINGERHUT H, GOZDAS E, HADI HOSSEINI S M. Quantitative MRI evidence for cognitive reserve in healthy Elders and prodromal Alzheimer's disease[J]. J Alzheimers Dis, 2022, 89(3): 849-863. DOI: 10.3233/JAD-220197.
[12]
SATO T, HANYU H, KOYAMA Y, et al. Discrepancy between the degree of cognitive impairment and brain imaging abnormalities in Alzheimer's disease patients is associated with cognitive reserve[J]. J Alzheimers Dis, 2021, 84(1): 273-281. DOI: 10.3233/JAD-210728.
[13]
PETERSEN R C, SMITH G E, WARING S C, et al. Mild cognitive impairment: clinical characterization and outcome[J]. Arch Neurol, 1999, 56(3): 303-308. DOI: 10.1001/archneur.56.3.303.
[14]
HESSEN E. Mild cognitive impairment and neuropsychological examination[J/OL]. Front Psychol, 2025, 16: 1662151 [2026-07-03]. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1662151/full. DOI: 10.3389/fpsyg.2025.1662151.
[15]
ISLAM N, HASHEM R, GAD M, et al. Accuracy of the Montreal Cognitive Assessment tool for detecting mild cognitive impairment: a systematic review and meta-analysis[J]. Alzheimers Dement, 2023, 19(7): 3235-3243. DOI: 10.1002/alz.13040.
[16]
TZOURIO-MAZOYER N, LANDEAU B, PAPATHANASSIOU D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. NeuroImage, 2002, 15(1): 273-289. DOI: 10.1006/nimg.2001.0978.
[17]
WANG J H, ZUO X N, GOHEL S, et al. Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data[J/OL]. PLoS One, 2011, 6(7): e21976 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/21818285/. DOI: 10.1371/journal.pone.0021976.
[18]
RUBINOV M, SPORNS O. Complex network measures of brain connectivity: uses and interpretations[J]. Neuroimage, 2010, 52(3): 1059-1069. DOI: 10.1016/j.neuroimage.2009.10.003.
[19]
LATORA V, MARCHIORI M. Efficient behavior of small-world networks[J/OL]. Phys Rev Lett, 2001, 87(19): 198701 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/11690461/. DOI: 10.1103/PhysRevLett.87.198701.
[20]
ACHARD S, BULLMORE E. Efficiency and cost of economical brain functional networks[J/OL]. PLoS Comput Biol, 2007, 3(2): e17 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/17274684/. DOI: 10.1371/journal.pcbi.0030017.
[21]
JANG H, MASHOUR G A, HUDETZ A G, et al. Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans[J/OL]. Nat Commun, 2024, 15: 9164 [2026-07-03]. https://www.nature.com/articles/s41467-024-53299-x. DOI: 10.1038/s41467-024-53299-x.
[22]
LI X Y, HAO H F, LI Y Y, et al. Menstrually-related migraine shapes the structural similarity network integration of brain[J]. Cereb Cortex, 2023, 33(17): 9867-9876. DOI: 10.1093/cercor/bhad250.
[23]
GAO J G, LIU L, YANG Z F, et al. Predictors of transition from mild cognitive impairment to normal cognition and dementia[J/OL]. Behav Sci, 2025, 15(11): 1552 [2026-07-03]. https://www.mdpi.com/2076-328X/15/11/1552. DOI: 10.3390/bs15111552.
[24]
CHEN H F, SHENG X N, LUO C M, et al. The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline[J/OL]. Transl Neurodegener, 2020, 9(1): 215 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/32460888/. DOI: 10.1186/s40035-020-00201-6.
[25]
SANABRIA-DIAZ G, MELIE-GARCIA L, DRAGANSKI B, et al. Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment[J/OL]. Sci Rep, 2021, 11: 845 [2026-07-03]. https://www.nature.com/articles/s41598-020-80909-7. DOI: 10.1038/s41598-020-80909-7.
[26]
ZHANG H Q, CHAU A C M, SHEA Y F, et al. Disrupted structural white matter network in Alzheimer's disease continuum, vascular dementia, and mixed dementia: a diffusion tensor imaging study[J]. J Alzheimers Dis, 2023, 94(4): 1487-1502. DOI: 10.3233/JAD-230341.
[27]
LIU Z Y, ZHANG Y M, YAN H, et al. Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer's disease: a resting-state fMRI study[J]. Psychiatry Res Neuroimaging, 2012, 202(2): 118-125. DOI: 10.1016/j.pscychresns.2012.03.002.
[28]
WANG B, MIAO L W, NIU Y, et al. Abnormal functional brain networks in mild cognitive impairment and Alzheimer's disease: a minimum spanning tree analysis[J]. J Alzheimers Dis, 2018, 65(4): 1093-1107. DOI: 10.3233/JAD-180603.
[29]
YAN S Z, ZHENG C J, CUI B X, et al. Multiparametric imaging hippocampal neurodegeneration and functional connectivity with simultaneous PET/MRI in Alzheimer's disease[J]. Eur J Nucl Med Mol Imag, 2020, 47(10): 2440-2452. DOI: 10.1007/s00259-020-04752-8.
[30]
WANG Z Q, LIANG P P, JIA X Q, et al. The baseline and longitudinal changes of PCC connectivity in mild cognitive impairment: a combined structure and resting-state fMRI study[J/OL]. PLoS One, 2012, 7(5): e36838 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/22629335/. DOI: 10.1371/journal.pone.0036838.
[31]
BUCKNER R L, ANDREWS-HANNA J R, SCHACTER D L. The brain's default network: anatomy, function, and relevance to disease[J/OL]. Ann N Y Acad Sci, 2008, 1124: 1-38 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/18400922/. DOI: 10.1196/annals.1440.011.
[32]
GORDON E M, LAUMANN T O, MAREK S, et al. Default-mode network streams for coupling to language and control systems[J]. Proc Natl Acad Sci USA, 2020, 117(29): 17308-17319. DOI: 10.1073/pnas.2005238117.
[33]
PINI L, WENNBERG A, MITOLO M, et al. Quality of sleep predicts increased frontoparietal network connectivity in patients with mild cognitive impairment[J/OL]. Neurobiol Aging, 2020, 95: 205-213 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/32846276/. DOI: 10.1016/j.neurobiolaging.2020.07.026.
[34]
ZHOU Y X, DOUGHERTY J H, HUBNER K F, et al. Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment[J]. Alzheimers Dement, 2008, 4(4): 265-270. DOI: 10.1016/j.jalz.2008.04.006.
[35]
ZHUANG Q, QIAO L, XU L, et al. The right inferior frontal gyrus as pivotal node and effective regulator of the basal ganglia-thalamocortical response inhibition circuit[J/OL]. Psychoradiology, 2023, 3: kkad016 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/38666118/. DOI: 10.1093/psyrad/kkad016.
[36]
HAGOORT P, INDEFREY P. The neurobiology of language beyond single words[J/OL]. Annu Rev Neurosci, 2014, 37: 347-362 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/24905595/. DOI: 10.1146/annurev-neuro-071013-013847.
[37]
AMINOFF E M, KVERAGA K, BAR M. The role of the parahippocampal cortex in cognition[J]. Trends Cogn Sci, 2013, 17(8): 379-390. DOI: 10.1016/j.tics.2013.06.009.
[38]
SHENG X N, CHEN H F, SHAO P F, et al. Brain structural network compensation is associated with cognitive impairment and Alzheimer's disease pathology[J/OL]. Front Neurosci, 2021, 15: 630278 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/33716654/. DOI: 10.3389/fnins.2021.630278.
[39]
WANG X N, ZENG Y, CHEN G Q, et al. Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment[J]. Oncotarget, 2016, 7(31): 48953-48962. DOI: 10.18632/oncotarget.10601.
[40]
LEISMAN G, BRAUN-BENJAMIN O, MELILLO R. Cognitive-motor interactions of the basal ganglia in development[J/OL]. Front Syst Neurosci, 2014, 8: 16 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/24592214/. DOI: 10.3389/fnsys.2014.00016.
[41]
ALEXANDER G E, DELONG M R, STRICK P L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex[J/OL]. Annu Rev Neurosci, 1986, 9: 357-381 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/3085570/. DOI: 10.1146/annurev.ne.09.030186.002041.
[42]
MIDDLETON F A, STRICK P L. Basal ganglia and cerebellar loops: motor and cognitive circuits[J]. Brain Res Brain Res Rev, 2000, 31(2/3): 236-250. DOI: 10.1016/s0165-0173(99)00040-5.
[43]
VIGNESWARAN C, NAIR S S, CHAKRAVARTHY V S. A Basal Ganglia model for understanding working memory functions in healthy and Parkinson's conditions[J]. Cogn Neurodyn, 2024, 18(4): 1913-1929. DOI: 10.1007/s11571-023-10056-y.
[44]
XIONG Y, YE C H, CHEN Y, et al. Altered functional connectivity of basal ganglia in mild cognitive impairment and Alzheimer's disease[J/OL]. Brain Sci, 2022, 12(11): 1555 [2026-07-03]. https://www.mdpi.com/2076-3425/12/11/1555. DOI: 10.3390/brainsci12111555.
[45]
ZHANG J H, LIU Y F, LAN K, et al. Gray matter atrophy in amnestic mild cognitive impairment: a voxel-based meta-analysis[J/OL]. Front Aging Neurosci, 2021, 13: 627919 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/33867968/. DOI: 10.3389/fnagi.2021.627919.
[46]
WEI C B, GONG S T, ZOU Q, et al. A comparative study of structural and metabolic brain networks in patients with mild cognitive impairment[J/OL]. Front Aging Neurosci, 2021, 13: 774607 [2026-07-03]. https://pubmed.ncbi.nlm.nih.gov/34938173/. DOI: 10.3389/fnagi.2021.774607.
[47]
ALAKÖRKKÖ T, SAARIMÄKI H, GLEREAN E, et al. Effects of spatial smoothing on functional brain networks[J]. Eur J Neurosci, 2017, 46(9): 2471-2480. DOI: 10.1111/ejn.13717.
[48]
TRIANA A M, GLEREAN E, SARAMÄKI J, et al. Effects of spatial smoothing on group-level differences in functional brain networks[J]. Netw Neurosci, 2020, 4(3): 556-574. DOI: 10.1162/netn_a_00132.

PREV Value of multi-delay arterial spin labeling in evaluating territorial perfusion injury in patients with posterior circulation ischemic stroke
NEXT Voxel‑based fMRI analysis of the effect of acupuncture on cerebral hemispheric lateralization in patients with ischemic stroke
  


LINK


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