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Altered modular organization of intrinsic brain functional networks in patients with Parkinson’s disease

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Abstract

Although previous studies reported altered topology of brain functional networks in patients with Parkinson’s disease (PD), the modular organization of brain functional networks in PD patients remains largely unknown. Using the resting-state functional MRI (R-fMRI) and graph theory, we examined the modular organization of brain functional networks in 32 unmedicated patients with early-to-mid motor stage PD and 31 healthy controls. Compared to the controls, the PD patients tended to show decreased integrity and segregation, both within and between modules. This was inferred by significantly increased intra-modular characteristic path length (L p) within four modules: mPFC, SN, SMN, and FPN, decreased inter-modular functional connectivity (FC) between mPFC and SN, SMN, and VN, and decreased intra-modular clustering in the PD patients. Intra-modular characteristic path length within the mPFC showed significantly positive correlation with general cognitive ability in the PD group. Receiver operating characteristic (ROC) analysis revealed that FC between mPFC and SN had the highest significant accuracy in differentiating the patients from the controls. Our findings may provide new insight in understanding the pathological changes that underlie impairment in cognition and movement in Parkinson’s disease.

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Abbreviations

mPFC:

Medial prefrontal cortex

SN:

Salience network

FPN:

Fronto-parietal network

SMN:

Somatomotor network

VN:

Visual network

pCER:

Posterior cerebellum

DMN:

Default mode network

References

  • 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. The Journal of Neuroscience, 26(1), 63–72.

    Article  CAS  PubMed  Google Scholar 

  • Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010). Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Frontiers in Systems Neuroscience, 4(147), 1–16.

    Google Scholar 

  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113.

    Article  PubMed  Google Scholar 

  • Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11(6), 805–821.

    Article  CAS  PubMed  Google Scholar 

  • Baggio, H. C., Sala-Llonch, R., Segura, B., Marti, M. J., Valldeoriola, F., Compta, Y., Tolosa, E., & Junqué, C. (2014). Functional brain networks and cognitive deficits in Parkinson’s disease. Human Brain Mapping, 35(9), 4620–4634.

    Article  PubMed  Google Scholar 

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 57, 289–300.

    Google Scholar 

  • Biundo, R., Calabrese, M., Weis, L., Facchini, S., Ricchieri, G., Gallo, P., & Antonini, A. (2013). Anatomical correlates of cognitive functions in early Parkinson’s disease patients. PloS One, 88, e64222.

    Article  Google Scholar 

  • Boller, F., Passafiume, D., Keefe, N. C., Rogers, K., Morrow, L., & Kim, Y. (1984). Visuospatial impairment in Parkinson’s disease: role of perceptual and motor factors. Archives of Neurology, 41(5), 485–490.

    Article  CAS  PubMed  Google Scholar 

  • Borghammer, P., Cumming, P., Østergaard, K., Gjedde, A., Rodell, A., Bailey, C. J., & Vafaee, M. S. (2012). Cerebral oxygen metabolism in patients with early Parkinson’s disease. Journal of the Neurological Sciences, 313(1), 123–128.

    Article  CAS  PubMed  Google Scholar 

  • Braak, H., Tredici, D. K., Rüb, U., de Vos, A. I. R., Steur, N. H. J. E., & Braak, E. (2003). Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiology of Aging, 24(2), 197–211.

    Article  PubMed  Google Scholar 

  • Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7(7), 113–140.

    Article  PubMed  Google Scholar 

  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10(3), 186–198.

    Article  CAS  PubMed  Google Scholar 

  • Cardoso, E. F., Maia, F. M., Fregni, F., Myczkowski, M. L., Melo, L. M., Sato, J. R., Marcolin, M. A., Rigonatti, S. P., Cruz Jr., A. C., & Barbosa, E. R. (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.

    Article  PubMed  Google Scholar 

  • Chen, Z. J., He, Y., Rosa-Neto, P., Gong, G., & Evans, A. C. (2011b). Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI. NeuroImage, 56(1), 235–245.

    Article  PubMed  Google Scholar 

  • Chen, G., Ward, B. D., Xie, C., Li, W., Wu, Z., Jones, J. L., Franczak, M., Antuono, P., & Li, S.-J. (2011a). Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology, 259(1), 213–221.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral Cortex, 18(10), 2374–2381.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen, G., Zhang, H.-Y., Xie, C., Chen, G., Zhang, Z.-J., Teng, G.-J., & Li, S.-J. (2013). Modular reorganization of brain resting state networks and its independent validation in Alzheimer’s disease patients. Frontiers in Human Neuroscience, 7, 456.

    PubMed  PubMed Central  Google Scholar 

  • Christopher, L., Marras, C., Duff-Canning, S., Koshimori, Y., Chen, R., Boileau, I., Segura, B., Monchi, O., Lang, A. E., & Rusjan, P. (2014). Combined insular and striatal dopamine dysfunction are associated with executive deficits in Parkinson’s disease with mild cognitive impairment. Brain, 137(Pt 2), 565–575.

    Article  PubMed  Google Scholar 

  • Cruse, D., Chennu, S., Chatelle, C., Bekinschtein, T. A., Fernandez-Espejo, D., Pickard, J. D., Laureys, S., & Owen, A. M. (2011). Bedside detection of awareness in the vegetative state: a cohort study. Lancet, 378(9809), 2088–2094.

    Article  PubMed  Google Scholar 

  • Dai, Z., Yan, C., Li, K., Wang, Z., Wang, J., Cao, M., Lin, Q., Shu, N., Xia, M., & Bi, Y. (2014). Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer’s Disease. Cerebral Cortex, 2014 epub bhu246.

  • Damoiseaux, J., Beckmann, C., Arigita, E. S., Barkhof, F., Scheltens, P., Stam, C., Smith, S., & Rombouts, S. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18(8), 1856–1864.

    Article  CAS  PubMed  Google Scholar 

  • Doyon, J., Gaudreau, D., Castonguay, M., Bedard, P., Bédard, F., & Bouchard, J. (1997). Role of the striatum, cerebellum, and frontal lobes in the learning of a visuomotor sequence. Brain and Cognition, 34(2), 218–245.

    Article  CAS  PubMed  Google Scholar 

  • Dubbelink, K. T. O., Hillebrand, A., Stoffers, D., Deijen, J. B., Twisk, J. W., Stam, C. J., & Berendse, H. W. (2013). Disrupted brain network topology in Parkinson’s disease: a longitudinal magnetoencephalography study. Brain, 137(1), 197–207.

    Article  Google Scholar 

  • Euston, D. R., Gruber, A. J., McNaughton, B. L. (2012). The role of medial prefrontal cortex in memory and decision making. Neuron, 76(6), 1057–1070.

  • Fearnley, J. M., & Lees, A. J. (1991). Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain, 114(5), 2283–2301.

    Article  PubMed  Google Scholar 

  • Ferri, F., Frassinetti, F., Ardizzi, M., Costantini, M., & Gallese, V. (2012). A sensorimotor network for the bodily self. Journal of Cognitive Neuroscience, 24(7), 1584–1595.

    Article  PubMed  Google Scholar 

  • Foti, N. J., Hughes, J. M., & Rockmore, D. N. (2011). Nonparametric sparsification of complex multiscale networks. PloS One, 6(2), e16431.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Friston, K. J., Williams, S., Howard, R., Frackowial, R. S., & Turner, R. (1996). Movement related effects in fMRI time series. Magnetic Resonance in Medicine, 35(3), 346–355.

    Article  CAS  PubMed  Google Scholar 

  • Gorges, M., Müller, H. P., Lulé, D., Consortium, L., Pinkhardt, E. H., Ludolph, A. C., & Kassubek, J. (2015). To rise and to fall: functional connectivity in cognitively normal and cognitively impaired patients with Parkinson’s disease. Neurobiology of Aging, 36(4), 1727–1735.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895–900.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • de Haan, W., van der Flier, W. M., Koene, T., Smits, L., Scheltens, P., & Stam, C. J. (2012). Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer’s disease. NeuroImage, 59(4), 3085–3093.

    Article  PubMed  Google Scholar 

  • Hawkes, C. H., Tredici, K. D., & Braak, H. (2009). Parkinson’s disease. Annals of the New York Academy of Sciences, 1170(1), 615–622.

    Article  PubMed  Google Scholar 

  • He, Y., Wang, J., Wang, L., Chen, Z. J., Yan, C., Yang, H., Tang, H., Zhu, C., Gong, Q., & Zang, Y. (2009). Uncovering intrinsic modular organization of spontaneous brain activity in humans. PloS One, 4, e5226.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Hoehn, M. M., & Yahr, M. D. (1998). Parkinsonism: onset, progression, and mortality. Neurology, 50(2), 318–318.

    Article  PubMed  Google Scholar 

  • Jenkins, A. C., & Mitchell, J. P. (2011). Medial prefrontal cortex subserves diverse forms of self-reflection. Social Neuroscience, 6(3), 211–218.

    Article  PubMed  Google Scholar 

  • Jucker, M., & Walker, L. C. (2013). Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature, 501(7465), 45–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kikuchi, A., Takeda, A., Kimpara, T., Nakagawa, M., Kawashima, R., Sugiura, M., Kinomura, S., Fukuda, H., Chida, K., & Okita, N. (2001). Hypoperfusion in the supplementary motor area, dorsolateral prefrontal cortex and insular cortex in Parkinson’s disease. Journal of the Neurological Sciences, 193(1), 29–36.

    Article  CAS  PubMed  Google Scholar 

  • Kurani, A. S., Seidler, R. D., Burciu, R. G., Comella, C. L., Corcos, D. M., Okun, M. S., MacKinnon, C. D., & Vaillancourt, D. E. (2015). Subthalamic nucleus—sensorimotor cortex functional connectivity in de novo and moderate Parkinson’s disease. Neurobiology of Aging, 36(1), 462–469.

    Article  PubMed  Google Scholar 

  • Lebedev, A. V., Westman, E., Simmons, A., Lebedeva, A., Siepel, F. J., Pereira, J. B., & Aarsland, D. (2014). Large-scale resting state network correlates of cognitive impairment in Parkinson’s disease and related dopaminergic deficits. Frontiers in Systems Neuroscience, 8, 45.

    PubMed  PubMed Central  Google Scholar 

  • Long, D., Wang, J., Xuan, M., Gu, Q., Xu, X., Kong, D., & Zhang, M. (2012). Automatic classification of early Parkinson’s disease with multi-modal MR imaging. PloS One, 7, e47714.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Luo, C. Y., Guo, X. Y., Song, W., Chen, Q., Cao, B., Yang, J., Gong, Q. Y., & Shang, H.-F. (2015). Functional connectome assessed using graph theory in drug-naive Parkinson’s disease. Journal of Neurology, 262(60), 1557–1567.

    Article  CAS  PubMed  Google Scholar 

  • Mesulam, M.-M. (1998). From sensation to cognition. Brain, 121(6), 1013–1052.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Newman, M. E. (2004). Analysis of weighted networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 70(5), 056131.

    Article  CAS  PubMed  Google Scholar 

  • Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 036104.

    Article  CAS  Google Scholar 

  • Polito, C., Berti, V., Ramat, S., Vanzi, E., De Cristofaro, M. T., Pellicanò, G., Mungai, F., Marini, P., Formiconi, A. R., & Sorbi, S. (2012). Interaction of caudate dopamine depletion and brain metabolic changes with cognitive dysfunction in early Parkinson’s disease. Neurobiology of Aging, 33(1), 206. e229–206. e239.

    Article  Google Scholar 

  • Pont-Sunyer, C., Hotter, A., Gaig, C., Seppi, K., Compta, Y., Katzenschlager, R., Mas, N., Hofeneder, D., Brücke, T., & Bayés, A. (2014). The onset of nonmotor symptoms in Parkinson’s disease (the ONSET PD Study). Movement Disorders, 30(2), 229–237.

    Article  PubMed  Google Scholar 

  • Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage, 105, 536–551.

    Article  PubMed  Google Scholar 

  • Putcha, D., Ross, R. S., Cronin-Golomb, A., Janes, A. C., & Stern, C. E. (2015). Altered intrinsic functional coupling between core neurocognitive networks in Parkinson’s disease. Neuroimage Clin, 7, 449–455.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pyatigorskaya, N., Gallea, C., Garcia-Lorenzo, D., Vidailhet, M., & Lehéricy, S. (2013). A review of the use of magnetic resonance imaging in Parkinson’s disease. Ther. Adv Neurol Disord, 2014, 7(4), 206–220.

    Google Scholar 

  • Radebaugh, T., & Khachaturian, Z. (1998). Consensus report of the Working Group on: molecular and biochemical markers of Alzheimer’s disease. Neurobiology of Aging, 19(2), 109–116.

    Article  Google Scholar 

  • Rae, C. L., Correia, M. M., Altena, E., Hughes, L. E., Barker, R. A., & Rowe, J. B. (2012). White matter pathology in Parkinson’s disease: the effect of imaging protocol differences and relevance to executive function. NeuroImage, 62(3), 1675–1684.

    Article  PubMed  PubMed Central  Google Scholar 

  • Raichle, M. E., & Snyder, A. Z. (2007). A default mode of brain function: a brief history of an evolving idea. NeuroImage, 37(4), 1083–1090.

    Article  PubMed  Google Scholar 

  • Rektorova, I., Biundo, R., Marecek, R., Weis, L., Aarsland, D., & Antonini, A. (2014). Grey matter changes in cognitively impaired Parkinson’s disease patients. PloS One, 9(1), e85595.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069.

    Article  PubMed  Google Scholar 

  • Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012). Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. NeuroImage, 60(1), 623–632.

    Article  PubMed  PubMed Central  Google Scholar 

  • Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience, 27(9), 2349–2356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Skidmore, F., Korenkevych, D., Liu, Y., He, G., Bullmore, E., & Pardalos, P. M. (2011). Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data. Neuroscience Letters, 499(1), 47–51.

    Article  CAS  PubMed  Google Scholar 

  • Somerville, L. H., Jones, R. M., Ruberry, E. J., Dyke, J. P., Glover, G., & Casey, B. (2013). The medial prefrontal cortex and the emergence of self-conscious emotion in adolescence. Psychological Science, 24(8), 1554–1562.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sporns, O. (2011). The non-random brain: efficiency, economy, and complex dynamics. Frontiers in Computational Neuroscience, 5(5), 1–13.

    Google Scholar 

  • Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2(2), 145–162.

    Article  PubMed  Google Scholar 

  • Tessitore, A., Esposito, F., Vitale, C., Santangelo, G., Amboni, M., Russo, A., Corbo, D., Cirillo, G., Barone, P., & Tedeschi, G. (2012). Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology, 79(23), 2226–2232.

    Article  PubMed  Google Scholar 

  • Tinaz, S., Lauro, P., Hallett, M., & Horovitz, S. G. (2015). Deficits in task-set maintenance and execution networks in Parkinson’s disease. Brain Structure and Function, 220(1), 1–13.

  • Vaessen, M., Braakman, H., Heerink, J., Jansen, J., Debeij-van Hall, M., Hofman, P., Aldenkamp, A., & Backes, W. (2013). Abnormal modular organization of functional networks in cognitively impaired children with frontal lobe epilepsy. Cerebral Cortex, 23(8), 1997–2006.

    Article  CAS  PubMed  Google Scholar 

  • Van Dijk, K. R., Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59(1), 431–438.

    Article  PubMed  Google Scholar 

  • van Eimeren, T., Monchi, O., Ballanger, B., & Strafella, A. P. (2009). Dysfunction of the default mode network in Parkinson disease: a functional magnetic resonance imaging study. Archives of Neurology, 66(7), 877–883.

    PubMed  PubMed Central  Google Scholar 

  • Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E., & Buckner, R. L. (2008). Evidence for a fronto-parietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology, 100(6), 3328–3342.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu, T., & Hallett, M. (2013). The cerebellum in Parkinson’s disease. Brain, 136(3), 696–709.

    Article  PubMed  Google Scholar 

  • Wu, T., Long, X., Wang, L., Hallett, M., Zang, Y., Li, K., & Chan, P. (2011b). Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease. Human Brain Mapping, 32(9), 1443–1457.

    Article  PubMed  Google Scholar 

  • Wu, K., Taki, Y., Sato, K., Sassa, Y., Inoue, K., Goto, R., Okada, K., Kawashima, R., He, Y., & Evans, A. C. (2011a). The overlapping community structure of structural brain network in young healthy individuals. PloS One, 6(5), e19608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Martino, A. D., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage, 76(1), 183–201.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010). Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage, 50(3), 970–983.

    Article  PubMed  Google Scholar 

  • Zuo, X.-N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., & Milham, M. P. (2012). Network centrality in the human functional connectome. Cerebral Cortex, 22(8), 1862–1875.

    Article  PubMed  Google Scholar 

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Correspondence to Biao Huang or Ruiwang Huang.

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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.

Funding

This study was funded by the National Natural Science Foundation of China (Grant numbers: 81271548, 81271560, 81371535, 81428013, and 81471654), and Zhejiang Provincial Natural Science Foundation of China (No. LZ13C090001).

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All of the authors declare no conflicts of interest.

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

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Highlights

• Characterized altered modular organization in PD patients at early-to-mid clinical motor stages.

• PD patients showed increased intra-modular path length in mPFC, SN, FPN, and SMN.

• Characteristic path length changed when confronted with ‘module lesion’

• Inter-modular functional connectivity between mPFC and SN can differentiate PD patients from controls.

Qing Ma and Biao Huang contributed equally to this work.

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Ma, Q., Huang, B., Wang, J. et al. Altered modular organization of intrinsic brain functional networks in patients with Parkinson’s disease. Brain Imaging and Behavior 11, 430–443 (2017). https://doi.org/10.1007/s11682-016-9524-7

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