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Predictive value of alterations of brain structural network topology in early-stage Parkinson's disease with mild cognitive impairment
ZHAO Xiaoyan  ZHANG Wei  ZHONG Weijia  GUO Dajing  LI Chuanming  ZHOU Baiwan  WU Xiaojia 

Cite this article as: Zhao XY, Zhang W, Zhong WJ, et al. Predictive value of alterations of brain structural network topology in early-stage Parkinson's disease with mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(3): 12-17, 70. DOI:10.12015/issn.1674-8034.2022.03.003.

[Abstract] Objective Useing diffusion tensor imaging (DTI) to explore the potential predictive value of changes of white matter (WM) structural network topological properties on mild cognitive impairment in early-stage Parkinson's disease (PD).Materials and Methods Eighty-three PD patients with normal cognition at baseline were included from the Parkinson's Progression Markers Initiative (PPMI) database, and all completed a 4-year follow-up. Among the 83 PD patients, 26 developed mild cognitive impairment (PD-MCI) and 57 retained normal cognition (PD-NC). Graph theory was utilized to evaluate the structural WM networks alterations in PD-MCI, and receiver operating characteristic analysis followed by stepwise logistic regression were performed to assess the predictive performance of network topology properties and cognitive measures.Results The patients with PD-MCI showed longitudinal decreased global efficiency and local efficiency, increased characteristic path length (P<0.05). Locally, patients with PD-MCI exhibited longitudinal reduced nodal centralities, mainly in the frontal, temporal, occipital, parietal and striatal-limbic system regions over time (P<0.05). Moreover, the longitudinal decline in the degree centrality and nodal efficiency of the right medial orbital superior frontal gyrus, and patient Montreal Cognitive Assessment and Letter–Number Sequencing scores predicted the development of cognitive impairment in early-stage PD (P<0.01).Conclusions The current study indicates that local network properties in the right medial orbital superior frontal gyrus can predict the onset of cognitive impairment in PD, and highlighting the value of network topology properties as sensitive biomarkers of cognitive decline in early-stage PD patients.
[Keywords] mild cognitive impairment;Parkinson's disease;diffusion tensor imaging;brain network;topological properties

ZHAO Xiaoyan   ZHANG Wei*   ZHONG Weijia   GUO Dajing   LI Chuanming   ZHOU Baiwan   WU Xiaojia  

Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China

Zhang W, E-mail:

Conflicts of interest   None.

Received  2021-07-24
Accepted  2022-02-21
DOI: 10.12015/issn.1674-8034.2022.03.003
Cite this article as: Zhao XY, Zhang W, Zhong WJ, et al. Predictive value of alterations of brain structural network topology in early-stage Parkinson's disease with mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(3): 12-17, 70.DOI:10.12015/issn.1674-8034.2022.03.003

Weil RS, Costantini AA, Schrag AE. Mild Cognitive Impairment in Parkinson's Disease-What Is It?[J]. Curr Neurol Neurosci Rep, 2018, 18(4): 17-28. DOI: 10.1007/s11910-018-0823-9.
Brandao PRP, Munhoz RP, Grippe TC, et al. Cognitive impairment in Parkinson's disease: A clinical and pathophysiological overview[J]. J Neurol Sci, 2020, 419: 117177-117192. DOI: 10.1016/j.jns.2020.117177.
Pedersen KF, Larsen JP, Tysnes OB, et al. Natural course of mild cognitive impairment in Parkinson disease: A 5-year population-based study[J]. Neurology, 2017, 88(8): 767-774. DOI: 10.1212/WNL.0000000000003634.
Pu W, Shen X, Huang M, et al. Assessment of White Matter Lesions in Parkinson's Disease: Voxel-based Analysis and Tract-based Spatial Statistics Analysis of Parkinson's Disease with Mild Cognitive Impairment[J]. Curr Neurovasc Res, 2020, 17(4): 480-486. DOI: 10.2174/1567202617666200901181842.
Zheng D, Chen C, Song W, et al. Regional gray matter reductions associated with mild cognitive impairment in Parkinson's disease: A meta-analysis of voxel-based morphometry studies[J]. Behav Brain Res, 2019, 371: 111973-111983. DOI: 10.1016/j.bbr.2019.111973.
Gorges M, Muller HP, Liepelt-Scarfone I, et al. Structural brain signature of cognitive decline in Parkinson's disease: DTI-based evidence from the LANDSCAPE study[J]. Ther Adv Neurol Disord, 2019, 12: 447-462. DOI: 10.1177/1756286419843447.
Wang W, Mei M, Gao Y, et al. Changes of brain structural network connection in Parkinson's disease patients with mild cognitive dysfunction: a study based on diffusion tensor imaging[J]. J Neurol, 2019, 267 (4):933-943. DOI: 10.1007/s00415-019-09645-x.
Pereira JB, Aarsland D, Ginestet CE, et al. Aberrant cerebral network topology and mild cognitive impairment in early Parkinson's disease[J]. Hum Brain Mapp, 2015, 36(8): 2980-2995. DOI: 10.1002/hbm.22822.
Galantucci S, Agosta F, Stefanova E, et al. Structural Brain Connectome and Cognitive Impairment in Parkinson Disease[J]. Radiology, 2017, 283(2): 515-525. DOI: 10.1148/radiol.2016160274.
Litvan I, Goldman JG, Troster AI, et al. Diagnostic criteria for mild cognitive impairment in Parkinson's disease: Movement Disorder Society Task Force guidelines[J]. Mov Disord, 2012, 27(3): 349-356. DOI: 10.1002/mds.24893.
Cui Z, Zhong S, Xu P, et al. PANDA: a pipeline toolbox for analyzing brain diffusion images[J]. Front Hum Neurosci, 2013, 7: 42. DOI: 10.3389/fnhum.2013.00042.
Wang J, Wang X, Xia M, et al. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics[J]. Front Hum Neurosci, 2015, 9: 386-402. DOI: 10.3389/fnhum.2015.00386.
Niu R, Lei D, Chen F, et al. Reduced local segregation of single-subject gray matter networks in adult PTSD[J]. Hum Brain Mapp, 2018, 39(12): 4884-4892. DOI: 10.1002/hbm.24330.
Suo X, Lei D, Chen F, et al. Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder[J]. Radiology, 2017, 282(3): 826-834. DOI: 10.1148/radiol.2016160907.
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks[J]. NeuroImage, 2010, 53 (4): 1197-1207. DOI: 10.1016/j.neuroimage.2010.06.041.
Sporns O. Graph theory methods: applications in brain networks[J]. Dialogues Clin Neurosci, 2018, 20 (2): 111-121. DOI: 10.31887/DCNS.2018.20.2/osporns.
Garcia RA, Marti AC, Cabeza C, et al. Small-worldness favours network inference in synthetic neural networks[J]. Sci Rep, 2020, 10(1): 2296-2306. DOI: 10.1038/s41598-020-59198-7.
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.
Bressler SL, Menon V. Large-scale brain networks in cognition: emerging methods and principles[J]. Trends Cogn Sci, 2010, 14(6): 277-290. DOI: 10.1016/j.tics.2010.04.004.
Putcha D, Ross RS, Cronin-Golomb A, et al. Salience and Default Mode Network Coupling Predicts Cognition in Aging and Parkinson's Disease[J]. J Int Neuropsychol Soc, 2016, 22(2): 205-215. DOI: 10.1017/S1355617715000892.
Hou Y, Wei Q, Ou R, et al. Impaired topographic organization in Parkinson's disease with mild cognitive impairment[J]. J Neurol Sci, 2020, 414: 116861-116869. DOI: 10.1016/j.jns.2020.116861.
Chen X, Liu M, Wu Z, et al. Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson's Disease Patients With Mild Cognitive Impairment[J]. Front Neurosci, 2020, 14: 616872-616879. DOI: 10.3389/fnins.2020.616872.
Sarwar T, Ramamohanarao K, Zalesky A. Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?[J]. Magn Reson Med, 2019, 81(2): 1368-1384. DOI: 10.1002/mrm.27471.

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