Advertisement

Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1202–1219 | Cite as

Effects of levodopa therapy on voxel-based degree centrality in Parkinson’s disease

  • Miao Zhong
  • Wanqun Yang
  • Biao HuangEmail author
  • Wenjie Jiang
  • Xiong Zhang
  • Xiaojin Liu
  • Lijuan Wang
  • Junjing Wang
  • Ling Zhao
  • Yuhu Zhang
  • Yingjun Liu
  • Jiabao Lin
  • Ruiwang HuangEmail author
Original Research

Abstract

Levodopa therapy is widely recognized as an effective treatment for PD patients, however, it is rare of the study looking at effects of levodopa therapy on the whole-brain network. This study was to evaluate the effects of levodopa on whole-brain degree centrality (DC) and seed-based functional connectivity (FC) in PD patients. We recruited 26 PD patients and acquired their resting-state fMRI data before (‘OFF’ state) and after (‘ON’ state) taking a dose of 400 mg levodopa. Through constructing the voxel-based brain functional network, we calculated distant and local DC and seed-based FC. We found that compared to the healthy controls, the PD patients at ‘OFF’ state showed significantly decreased distant DC in several occipital regions and left postcentral gyrus, but increased distant DC in the right precentral gyrus, supplementary motor area, and several frontal regions. Meanwhile, we detected decreased local DC in the left cuneus and bilateral insula but increased local DC in several temporal regions in the PD patients at ‘OFF’ state compared to the controls. Using paired-sample t-tests, we found that levodopa effectively normalized the distant DC abnormalities in the PD patients particularly in the occipital regions and postcentral gyrus. Additionally, compared to ‘OFF’ state, the PD patients at ‘ON’ state showed decreased FC of the left median cingulate gyrus to brain regions in default mode network. The decreased FC of the left median cingulate gyrus to right temporal pole was associated with improved UPDRS-III score. This study provided new evidence for understanding the neural effects of levodopa therapy on the whole-brain network in PD patients.

Keywords

Resting-state fMRI Network centrality Distant DC Local DC Functional connectivity 

Abbreviations

ALFF

amplitude of low-frequency oscillations

ANG

angular gyrus

CUN

cuneus

DC

degree centrality

DMN

default mode network

FWHM

Full-Width at Half Maximum

FC

functional connectivity

H-Y

Hoehn and Yahr stage

ICA

independent component analysis

INS

insula

IFGoperc

opercular part of inferior frontal gyrus

IFGtriang

triangular part of inferior frontal gyrus

IOG

inferior occipital gyrus

LING

lingual gyrus

MMSE

Mini-Mental State Examination

MFG

middle frontal gyrus

MTG

middle temporal gyrus

MCG

median cingulate gyrus

MOG

middle occipital gyrus

MCI

mild cognitive impairment

ORBinf

orbital part of inferior frontal gyrus

PMC

premotor cortex

PoCG

postcentral gyrus

PreCG

precentral gyrus

PHG

parahippocampal gyrus

PCUN

precuneus

ReHo

regional homogeneity

ROI

region of interest

SMA

supplementary motor area

STG

superior temporal gyrus

SFGmed

medial superior frontal gyrus

SOG

superior occipital gyrus

TPOmid

temporal pole of middle temporal gyrus

THA

thalamus

UPDRS-III

Unified Parkinson’s Disease Rating Scale-motor

Symbol

‘OFF’ state

before levodopa therapy

‘ON’ state

after levodopa therapy

DCi

degree centrality for a given voxel i

dij

connection or edge weight from voxel i to voxel j

\( \overline{DC} \)

mean degree across all voxels in the whole brain degree centrality map

σDC

standard deviation of degree centrality

zDC

z-score of degree centrality

szDC

smoothed z-score of degree centrality

zFC

z-score of functional connectivity

ΔDC

difference in degree centrality between ‘ON’ and ‘OFF’ state

ΔFC

difference in functional connectivity between ‘ON’ and ‘OFF’ state

ΔUPDRS-III

difference in UPDRS-III score between ‘ON’ and ‘OFF’ state

ΔMMSE

difference in MMSE score between ‘ON’ and ‘OFF’ state

Notes

Acknowledgements

The authors are grateful to the two anonymous reviewers for constructive and insightful comments on a previous version of this article.

Funding

This work was supported by the funding from the Natural Science Foundation of China (Grant numbers: 81271548, 81271560, 81371535, 81428013, and 81471654) and Planned Science and Technology Project of Guangzhou, China (Grant numbers: 20160402007).

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interest

All of the authors declare no conflicts of interest.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2018_9936_MOESM1_ESM.png (5.3 mb)
Fig. S1 (PNG 5457 kb)
11682_2018_9936_MOESM2_ESM.png (5.4 mb)
Fig. S2 (PNG 5532 kb)

References

  1. Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26(1), 63–72.Google Scholar
  2. Anderson, J. S., Druzgal, T. J., Lopez-Larson, M., Jeong, E. K., Desai, K., & Yurgelun-Todd, D. (2011). Network anticorrelations, global regression, and phase-shifted soft tissue correction. Human Brain Mapping, 32(6), 919–934.Google Scholar
  3. Bell, P. T., Gilat, M., O'Callaghan, C., Copland, D. A., Frank, M. J., Lewis, S. J., et al. (2015). Dopaminergic basis for impairments in functional connectivity across subdivisions of the striatum in Parkinson's disease. Human Brain Mapping, 36(4), 1278–1291.Google Scholar
  4. Beucke, J. C., Sepulcre, J., Talukdar, T., Linnman, C., Zschenderlein, K., Endrass, T., et al. (2013). Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder. JAMA Psychiatry, 70(6), 619–629.Google Scholar
  5. Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006). Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage, 31(4), 1536–1548.Google Scholar
  6. Braak, H., Del Tredici, K., Rüb, U., De Vos, R. A., Steur, E. N. J., & Braak, E. (2003). Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiology of Aging, 24(2), 197–211.Google Scholar
  7. Braak, H., Bohl, J. R., Müller, C. M., Rüb, U., de Vos, R. A., & Del Tredici, K. (2006). Stanley Fahn Lecture 2005: The staging procedure for the inclusion body pathology associated with sporadic Parkinson's disease reconsidered. Movement Disorders, 21(12), 2042–2051.Google Scholar
  8. Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290.Google Scholar
  9. Buccino, G., Vogt, S., Ritzl, A., Fink, G. R., Zilles, K., Freund, H.-J., et al. (2004). Neural circuits underlying imitation learning of hand actions: an event-related fMRI study. Neuron, 42(2), 323–334.Google Scholar
  10. Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. Journal of Neuroscience, 29(6), 1860–1873.Google Scholar
  11. Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10(3), 186–198.Google Scholar
  12. Cardoso, E. F., Maia, F. M., Fregni, F., Myczkowski, M. L., Melo, L. M., Sato, J. R., et al. (2009). Depression in Parkinson's disease: convergence from voxel-based morphometry and functional magnetic resonance imaging in the limbic thalamus. Neuroimage, 47(2), 467–472.Google Scholar
  13. Cha, J., Ide, J. S., Bowman, F. D., Simpson, H. B., Posner, J., & Steinglass, J. E. (2016). Abnormal reward circuitry in anorexia nervosa: A longitudinal, multimodal MRI study. Human Brain Mapping, 37(11), 3835–3846.Google Scholar
  14. Chang, C., & Glover, G. H. (2009). Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage, 47(4), 1448–1459.Google Scholar
  15. Chen, Y., Pressman, P., Simuni, T., Parrish, T. B., & Gitelman, D. R. (2015). Effects of acute levodopa challenge on resting cerebral blood flow in Parkinson’s Disease patients assessed using pseudo-continuous arterial spin labeling. PeerJ, 3, e1381.Google Scholar
  16. Chéron, G., Dan, B., & Borenstein, S. (2000). Sensory and motor interfering influences on somatosensory evoked potentials. Journal of Clinical Neurophysiology, 17(3), 280–294.Google Scholar
  17. Christopher, L., Koshimori, Y., Lang, A. E., Criaud, M., & Strafella, A. P. (2014a). Uncovering the role of the insula in non-motor symptoms of Parkinson’s disease. Brain, 137(8), 2143–2154.Google Scholar
  18. Christopher, L., Marras, C., Duff-Canning, S., Koshimori, Y., Chen, R., Boileau, I., et al. (2014b). Combined insular and striatal dopamine dysfunction are associated with executive deficits in Parkinson’s disease with mild cognitive impairment. Brain, 137(2), 565–575.Google Scholar
  19. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155.Google Scholar
  20. Contin, M., & Martinelli, P. (2010). Pharmacokinetics of levodopa. Journal of Neurology, 257(2), 253–261.Google Scholar
  21. Di Martino, A., Zuo, X.-N., Kelly, C., Grzadzinski, R., Mennes, M., Schvarcz, A., et al. (2013). Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder. Biological Psychiatry, 74(8), 623–632.Google Scholar
  22. Diederich, N. J., Stebbins, G., Schiltz, C., & Goetz, C. G. (2014). Are patients with Parkinson’s disease blind to blindsight? Brain, 137(6), 1838–1849.Google Scholar
  23. Dubbelink, K. T. O., Hillebrand, A., Stoffers, D., Deijen, J. B., Twisk, J. W., Stam, C. J., et al. (2013). Disrupted brain network topology in Parkinson’s disease: a longitudinal magnetoencephalography study. Brain, 137(1), 197–207.Google Scholar
  24. Dubbelink, K. T. O., Schoonheim, M. M., Deijen, J. B., Twisk, J. W., Barkhof, F., & Berendse, H. W. (2014). Functional connectivity and cognitive decline over 3 years in Parkinson disease. Neurology, 83(22), 2046–2053.Google Scholar
  25. Esposito, F., Tessitore, A., Giordano, A., De Micco, R., Paccone, A., Conforti, R., et al. (2013). Rhythm-specific modulation of the sensorimotor network in drug-naive patients with Parkinson’s disease by levodopa. Brain, 136(3), 710–725.Google Scholar
  26. Fahn, S. (1999). Parkinson disease, the effect of levodopa, and the ELLDOPA trial. Archives of Neurology, 56(5), 529–535.Google Scholar
  27. Fahn, S., & Elton, R. (1987). Unified rating scale for Parkinson’s disease. (pp. 153–163): Macmillan, Florham Park, New York.Google Scholar
  28. Fang, J., Chen, H., Cao, Z., Jiang, Y., Ma, L., Ma, H., et al. (2017). Impaired brain network architecture in newly diagnosed Parkinson’s disease based on graph theoretical analysis. Neuroscience Letters, 657, 151–158.Google Scholar
  29. Felger, J. C., Li, Z., Haroon, E., Woolwine, B. J., Jung, M. Y., Hu, X., et al. (2016). Inflammation is associated with decreased functional connectivity within corticostriatal reward circuitry in depression. Molecular Psychiatry, 21(10), 1358.Google Scholar
  30. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.Google Scholar
  31. Forno, L. S. (1981). Pathology of Parkinson's disease. In Movement Disorders (pp. 25–40): Elsevier.Google Scholar
  32. Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology, 101(6), 3270–3283.Google Scholar
  33. Gallagher, D. A., Parkkinen, L., O'sullivan, S. S., Spratt, A., Shah, A., Davey, C. C., et al. (2011). Testing an aetiological model of visual hallucinations in Parkinson's disease. Brain, 134(11), 3299–3309.Google Scholar
  34. Gao, L. L., Zhang, J. R., Chan, P., & Wu, T. (2017). Levodopa Effect on Basal Ganglia Motor Circuit in Parkinson's Disease. CNS Neuroscience & Therapeutics, 23(1), 76–86.Google Scholar
  35. Gibb, W., & Lees, A. (1988). The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease. Journal of Neurology, Neurosurgery and Psychiatry, 51(6), 745–752.Google Scholar
  36. Göttlich, M., Münte, T. F., Heldmann, M., Kasten, M., Hagenah, J., & Krämer, U. M. (2013). Altered resting state brain networks in Parkinson’s disease. PLoS One, 8(10), e77336.Google Scholar
  37. Group, P. S. (2004). Levodopa and the progression of Parkinson's disease. New England Journal of Medicine, 351(24), 2498–2508.Google Scholar
  38. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., et al. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.Google Scholar
  39. Harding, A., Broe, G., & Halliday, G. (2002). Visual hallucinations in Lewy body disease relate to Lewy bodies in the temporal lobe. Brain, 125(2), 391–403.Google Scholar
  40. Hayasaka, S. (2013). Functional connectivity networks with and without global signal correction. Frontiers in Human Neuroscience, 7, 880.Google Scholar
  41. Helmich, R. C., Derikx, L. C., Bakker, M., Scheeringa, R., Bloem, B. R., & Toni, I. (2010). Spatial remapping of cortico-striatal connectivity in Parkinson's disease. Cerebral Cortex, 20(5), 1175–1186.Google Scholar
  42. Hoehn, M. M., & Yahr, M. D. (1998). Parkinsonism: onset, progression, and mortality. Neurology, 50(2), 318–318.Google Scholar
  43. Hou, Y., Wu, X., Hallett, M., Chan, P., & Wu, T. (2014). Frequency-dependent neural activity in Parkinson's disease. Human Brain Mapping, 35(12), 5815–5833.Google Scholar
  44. Hu, X., Song, X., Li, E., Liu, J., Yuan, Y., Liu, W., et al. (2015). Altered resting-state brain activity and connectivity in depressed Parkinson’s disease. PLoS One, 10(7), e0131133.Google Scholar
  45. Jankovic, J. (2008). Parkinson’s disease: clinical features and diagnosis. Journal of Neurology, Neurosurgery and Psychiatry, 79(4), 368–376.Google Scholar
  46. Jenkins, I. H., Jahanshahi, M., Jueptner, M., Passingham, R. E., & Brooks, D. J. (2000). Self-initiated versus externally triggered movements: II. The effect of movement predictability on regional cerebral blood flow. Brain, 123(6), 1216–1228.Google Scholar
  47. Jubault, T., Gagnon, J.-F., Karama, S., Ptito, A., Lafontaine, A.-L., Evans, A. C., et al. (2011). Patterns of cortical thickness and surface area in early Parkinson's disease. Neuroimage, 55(2), 462–467.Google Scholar
  48. Kelly, C., de Zubicaray, G., Di Martino, A., Copland, D. A., Reiss, P. T., Klein, D. F., et al. (2009). L-dopa modulates functional connectivity in striatal cognitive and motor networks: a double-blind placebo-controlled study. Journal of Neuroscience, 29(22), 7364–7378.Google Scholar
  49. Kempster, P. A., O’Sullivan, S. S., Holton, J. L., Revesz, T., & Lees, A. J. (2010). Relationships between age and late progression of Parkinson’s disease: a clinico-pathological study. Brain, 133(6), 1755–1762.Google Scholar
  50. Kish, S. J., Shannak, K., & Hornykiewicz, O. (1988). Uneven pattern of dopamine loss in the striatum of patients with idiopathic Parkinson's disease. New England Journal of Medicine, 318(14), 876–880.Google Scholar
  51. Krajcovicova, L., Mikl, M., Marecek, R., & Rektorova, I. (2012). The default mode network integrity in patients with Parkinson’s disease is levodopa equivalent dose-dependent. Journal of Neural Transmission, 119(4), 443–454.Google Scholar
  52. Kurani, A. S., Seidler, R. D., Burciu, R. G., Comella, C. L., Corcos, D. M., Okun, M. S., et al. (2014). Subthalamic nucleus-sensorimotor cortex functional connectivity in de novo and moderate Parkinson's disease. Neurobiology of Aging, 36(1), 462–469.Google Scholar
  53. Kwak, Y., Peltier, S., Bohnen, N., Müller, M., Dayalu, P., & Seidler, R. D. (2010). Altered resting state cortico-striatal connectivity in mild to moderate stage Parkinson's disease. Frontiers in Systems Neuroscience, 4, 143.Google Scholar
  54. Kwak, Y., Peltier, S., Bohnen, N., Müller, M., Dayalu, P., & Seidler, R. D. (2012). L-DOPA changes spontaneous low-frequency BOLD signal oscillations in Parkinson's disease: a resting state fMRI study. Frontiers in Systems Neuroscience, 6, 52.Google Scholar
  55. Ledberg, A., Åkerman, S., & Roland, P. E. (1998). Estimation of the probabilities of 3D clusters in functional brain images. Neuroimage, 8(2), 113–128.Google Scholar
  56. Liu, W., Wei, D., Sun, J., Yang, J., Meng, J., Wang, L., et al. (2015). Abnormal Degree Centrality of Functional Hubs Associated with Negative Coping in Older Chinese Adults Who Lost Their Only Child. Biological Psychology, 112, 46–55.Google Scholar
  57. Luo, C., Guo, X., Song, W., Chen, Q., Yang, J., Gong, Q., et al. (2015). The trajectory of disturbed resting-state cerebral function in Parkinson's disease at different Hoehn and Yahr stages. Human Brain Mapping, 36(8), 3104–3116.Google Scholar
  58. Melzer, T. R., Watts, R., MacAskill, M. R., Pitcher, T. L., Livingston, L., Keenan, R. J., et al. (2012). Grey matter atrophy in cognitively impaired Parkinson's disease. Journal of Neurology, Neurosurgery and Psychiatry, 83(2), 188–194.Google Scholar
  59. Meppelink, A. M., de Jong, B. M., Renken, R., Leenders, K. L., Cornelissen, F. W., & van Laar, T. (2009). Impaired visual processing preceding image recognition in Parkinson's disease patients with visual hallucinations. Brain, 132(11), 2980–2993.Google Scholar
  60. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167–202.Google Scholar
  61. Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009). The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage, 44(3), 893–905.Google Scholar
  62. Oldfield, R. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 9(1), 97–113.Google Scholar
  63. Pereira, J. B., Aarsland, D., Ginestet, C. E., Lebedev, A. V., Wahlund, L. O., Simmons, A., et al. (2015). Aberrant cerebral network topology and mild cognitive impairment in early Parkinson's disease. Human Brain Mapping, 36(8), 2980–2995.Google Scholar
  64. Qing, Z., Dong, Z., Li, S., Zang, Y., & Liu, D. (2015). Global signal regression has complex effects on regional homogeneity of resting state fMRI signal. Magnetic Resonance Imaging, 33(10), 1306–1313.Google Scholar
  65. Rice, K., Moraczewski, D., & Redcay, E. (2016). Perceived live interaction modulates the developing social brain. Social Cognitive and Affective Neuroscience, 11(9), 1354–1362.Google Scholar
  66. Roland, P. E., Larsen, B., Lassen, N. A., & Skinhoj, E. (1980). Supplementary motor area and other cortical areas in organization of voluntary movements in man. Journal of Neurophysiology, 43(1), 118–136.Google Scholar
  67. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069.Google Scholar
  68. Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., et al. (2012). Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connectivity, 2(1), 25–32.Google Scholar
  69. Salat, D., & Tolosa, E. (2013). Levodopa in the treatment of Parkinson's disease: current status and new developments. Journal of Parkinson's Disease, 3(3), 255–269.Google Scholar
  70. Schölvinck, M. L., Maier, A., Frank, Q. Y., Duyn, J. H., & Leopold, D. A. (2010). Neural basis of global resting-state fMRI activity. Proceedings of the National Academy of Sciences of the United States of America, 107(22), 10238–10243.Google Scholar
  71. Sepulcre, J., Liu, H., Talukdar, T., Martincorena, I., Yeo, B. T., & Buckner, R. L. (2010). The organization of local and distant functional connectivity in the human brain. PLoS Computational Biology, 6(6), e1000808.Google Scholar
  72. Shine, J. M., Halliday, G. M., Gilat, M., Matar, E., Bolitho, S. J., Carlos, M., et al. (2014). The role of dysfunctional attentional control networks in visual misperceptions in Parkinson's disease. Human Brain Mapping, 35(5), 2206–2219.Google Scholar
  73. Skidmore, F., Yang, M., Baxter, L., Von Deneen, K., Collingwood, J., He, G., et al. (2013). Reliability analysis of the resting state can sensitively and specifically identify the presence of Parkinson disease. Neuroimage, 75, 249–261.Google Scholar
  74. Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Hashizume, H., Sassa, Y., et al. (2015). Degree centrality and fractional amplitude of low-frequency oscillations associated with Stroop interference. Neuroimage, 119, 197–209.Google Scholar
  75. Tanji, J., & Hoshi, E. (2001). Behavioral planning in the prefrontal cortex. Current Opinion in Neurobiology, 11(2), 164–170.Google Scholar
  76. Tessitore, A., Amboni, M., Esposito, F., Russo, A., Picillo, M., Marcuccio, L., et al. (2012a). Resting-state brain connectivity in patients with Parkinson's disease and freezing of gait. Parkinsonism & Related Disorders, 18(6), 781–787.Google Scholar
  77. Tessitore, A., Esposito, F., Vitale, C., Santangelo, G., Amboni, M., Russo, A., et al. (2012b). Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology, 79(23), 2226–2232.Google Scholar
  78. Tomasi, D., & Volkow, N. D. (2010). Functional connectivity density mapping. Proceedings of the National Academy of Sciences of the United States of America, 107(21), 9885–9890.Google Scholar
  79. Tuovinen, N., Seppi, K., de Pasquale, F., Müller, C., Nocker, M., Schocke, M., et al. (2018). The reorganization of functional architecture in the early-stages of Parkinson's disease. Parkinsonism & Related Disorders.Google Scholar
  80. Uc, E., Rizzo, M., Anderson, S., Qian, S., Rodnitzky, R., & Dawson, J. (2005). Visual dysfunction in Parkinson disease without dementia. Neurology, 65(12), 1907–1913.Google Scholar
  81. Vogt, B. A., Berger, G. R., & Derbyshire, S. W. (2003). Structural and functional dichotomy of human midcingulate cortex. European Journal of Neuroscience, 18(11), 3134–3144.Google Scholar
  82. Wang, J. H., Zuo, X. N., Gohel, S., Milham, M. P., Biswal, B. B., & He, Y. (2011). Graph theoretical analysis of functional brain networks: test-retest evaluation on short-and long-term resting-state functional MRI data. PLoS One, 6(7), e21976.Google Scholar
  83. Wang, L. F., Dai, Z. J., Peng, H. J., Tan, L. W., Ding, Y. Q., He, Z., et al. (2014). Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Human Brain Mapping, 35(4), 1154–1166.Google Scholar
  84. Wang, L., Xia, M., Li, K., Zeng, Y., Su, Y., Dai, W., et al. (2015). The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Human Brain Mapping, 36(2), 768–778.Google Scholar
  85. Weissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., & Windischberger, C. (2009). Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage, 47(4), 1408–1416.Google Scholar
  86. Wu, T., & Hallett, M. (2005). A functional MRI study of automatic movements in patients with Parkinson's disease. Brain, 128(10), 2250–2259.Google Scholar
  87. Wu, T., Long, X., Zang, Y., Wang, L., Hallett, M., Li, K., et al. (2009a). Regional homogeneity changes in patients with Parkinson's disease. Human Brain Mapping, 30(5), 1502–1510.Google Scholar
  88. Wu, T., Wang, L., Chen, Y., Zhao, C., Li, K., & Chan, P. (2009b). Changes of functional connectivity of the motor network in the resting state in Parkinson's disease. Neuroscience Letters, 460(1), 6–10.Google Scholar
  89. Wu, T., Long, X., Wang, L., Hallett, M., Zang, Y., Li, K., et al. (2011). Functional connectivity of cortical motor areas in the resting state in Parkinson's disease. Human Brain Mapping, 32(9), 1443–1457.Google Scholar
  90. Wu, X., Zou, Q., Hu, J., Tang, W., Mao, Y., Gao, L., et al. (2015). Intrinsic functional connectivity patterns predict consciousness level and recovery outcome in acquired brain injury. Journal of Neuroscience, 35(37), 12932–12946.Google Scholar
  91. Xie, C. l., Zhang, Y. Y., Wang, X. D., Chen, J., Chen, Y. H., Pa, J. L., et al. (2015). Levodopa alone compared with levodopa-sparing therapy as initial treatment for Parkinson’s disease: a meta-analysis. Neurological Sciences, 36(8), 1319–1329.Google Scholar
  92. Yan, C. G., & Zang, Y. F. (2010). DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.Google Scholar
  93. Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics, 14(3), 339.Google Scholar
  94. Young, D. E., Wagenaar, R. C., Lin, C.-C., Chou, Y.-H., Davidsdottir, S., Saltzman, E., et al. (2010). Visuospatial perception and navigation in Parkinson’s disease. Vision Research, 50(23), 2495–2504.Google Scholar
  95. Zarei, M., Ibarretxe-Bilbao, N., Compta, Y., Hough, M., Junque, C., Bargallo, N., et al. (2013). Cortical thinning is associated with disease stages and dementia in Parkinson's disease. Journal of Neurology, Neurosurgery and Psychiatry, 84(8), 875–882.Google Scholar
  96. Zhang, J., Wei, L., Hu, X., Zhang, Y., Zhou, D., Li, C., et al. (2013). Specific frequency band of amplitude low-frequency fluctuation predicts Parkinson's disease. Behavioural Brain Research, 252, 18–23.Google Scholar
  97. Zhang, J., Bi, W., Zhang, Y., Zhu, M., Zhang, Y., Feng, H., et al. (2015). Abnormal functional connectivity density in Parkinson's disease. Behavioural Brain Research, 280, 113–118.Google Scholar
  98. Zuo, X. N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., et al. (2012). Network centrality in the human functional connectome. Cerebral Cortex, 22(8), 1862–1875.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Miao Zhong
    • 1
  • Wanqun Yang
    • 2
  • Biao Huang
    • 2
    Email author
  • Wenjie Jiang
    • 1
  • Xiong Zhang
    • 3
  • Xiaojin Liu
    • 1
  • Lijuan Wang
    • 3
  • Junjing Wang
    • 1
    • 5
  • Ling Zhao
    • 1
  • Yuhu Zhang
    • 3
  • Yingjun Liu
    • 4
  • Jiabao Lin
    • 1
  • Ruiwang Huang
    • 1
    Email author
  1. 1.School of Psychology, Institute of Brain Science and Rehabilitation, Key Laboratory of Mental Health and Cognitive Science of Guangdong ProvinceSouth China Normal UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of Radiology, Guangdong Academy of Medical SciencesGuangdong General HospitalGuangzhouPeople’s Republic of China
  3. 3.Department of Neurology, Guangdong Academy of Medical SciencesGuangdong General HospitalGuangzhouPeople’s Republic of China
  4. 4.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouPeople’s Republic of China
  5. 5.Department of Applied PsychologyGuangdong University of Foreign StudiesGuangzhouPeople’s Republic of China

Personalised recommendations