Advertisement

Active information storage in Parkinson’s disease: a resting state fMRI study over the sensorimotor cortex

  • Aura Cristina Puche SarmientoEmail author
  • Yamile Bocanegra García
  • John Fredy Ochoa Gómez
ORIGINAL RESEARCH

Abstract

Parkinson’s disease (PD), the second most frequent neurodegenerative disease, affects significantly life quality by a combination of motor and cognitive disturbances. Although it is traditionally associated with basal ganglia dysfunction, cortical alterations are also involved in disease symptoms. Our objective is to evaluate the alterations in brain dynamics in de novo and recently treated PD subjects using a nonlinear method known as Active Information Storage. In the current research, Active Information Storage (AIS) was used to study the complex dynamics in motor cortex spontaneous activity captured using resting state functional Magnetic Resonance Imaging (rs-fMRI) at early-stage in non-medicated and recently medicated PD subjects. Supplementary to AIS, the fractional Amplitude of Low Frequency Fluctuation (fALFF), which is a better-established technique of analysis of rs-fMRI signals, was also evaluated. Compared to healthy subjects, the AIS values were significantly reduced in PD patients over the analyzed motor cortex regions; differences were also found at less extent using the fALFF measure. Correlations between AIS and fALFF values showed that the measures seem to capture similar neuronal phenomena in rs-fMRI data. The highest sensitivity when detecting group differences revealed by AIS, and not captured by traditional linear approaches, suggests that this measure is a promising tool for the analysis of rs-fMRI neural data in PD.

Keywords

Parkinson’s disease Active information storage Resting-state functional magnetic resonance imaging Fractional amplitude of low frequency fluctuation Motor cortex 

Notes

Acknowledgements

The research over the data was supported by Vicerrectoría de Investigación de la Universidad de Antioquia (CODI), project “Neurofisiología y Neuropsicología en Enfermedad Ganglio Basal”, code PRG2014-768. Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI – a public-private partnership –funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, General Electric Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal Imaging, Roche, Sanofi-Genzyme, Servier, Takeda, Teva and UCB.

Funding

This study was funded by the Universidad de Antioquia under the code PRG2014–768.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interests.

Ethical approval

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

Informed consent

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

Supplementary material

11682_2019_37_MOESM1_ESM.docx (17 kb)
ESM 1 (DOCX 17 kb)
11682_2019_37_MOESM2_ESM.docx (19 kb)
ESM 2 (DOCX 19 kb)

References

  1. Adeli, E., Shi, F., An, L., Wee, C. Y., Wu, G., Wang, T., & Shen, D. (2016). Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. NeuroImage, 141, 206–219.  https://doi.org/10.1016/j.neuroimage.2016.05.054.CrossRefGoogle Scholar
  2. Alexander, G. E., Crutcher, M. D., & DeLong, M. R. (1990). Basal ganglia-thalamocortical circuits: Parallel substrates for motor, oculomotor, “prefrontal” and “limbic” functions. Progress in Brain Research, 85, 119–146 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2094891.CrossRefGoogle Scholar
  3. An, L., Cao, Q.-J., Sui, M.-Q., Sun, L., Zou, Q.-H., Zang, Y.-F., & Wang, Y.-F. (2013). Local synchronization and amplitude of the fluctuation of spontaneous brain activity in attention-deficit/hyperactivity disorder: A resting-state fMRI study. Neuroscience Bulletin, 29(5), 603–613.  https://doi.org/10.1007/s12264-013-1353-8.CrossRefGoogle Scholar
  4. Ashburner, J. (2012). SPM: A history. NeuroImage, 62(2), 791–800.  https://doi.org/10.1016/j.neuroimage.2011.10.025.CrossRefGoogle Scholar
  5. Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., & Hyde, J. S. (1992). Time course EPI of human brain function during task activation. Magnetic Resonance in Medicine, 25(2), 390–397 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1614324.CrossRefGoogle Scholar
  6. Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology, 22(4), 340–347.  https://doi.org/10.1097/WCO.0b013e32832d93dd.CrossRefGoogle Scholar
  7. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Source: Journal of the Royal Statistical Society. Series B (methodological) (Vol. 57). Retrieved from http://engr.case.edu/ray_soumya/mlrg/controlling_fdr_benjamini95.pdf
  8. Biomarkers Definitions Working Group. (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology & Therapeutics, 69(3), 89–95.  https://doi.org/10.1067/mcp.2001.113989.CrossRefGoogle Scholar
  9. Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8524021.CrossRefGoogle Scholar
  10. Brodski-Guerniero, A., Paasch, G.-F., Wollstadt, P., Özdemir, I., Lizier, J. T., & Wibral, M. (2017). Information-theoretic evidence for predictive coding in the face-processing system. The Journal of Neuroscience, 37(34), 8273–8283.  https://doi.org/10.1523/JNEUROSCI.0614-17.2017.CrossRefGoogle Scholar
  11. Burciu, R. G., Chung, J. W., Shukla, P., Ofori, E., Li, H., McFarland, N. R., Okun, M. S., & Vaillancourt, D. E. (2016). Functional MRI of disease progression in Parkinson disease and atypical parkinsonian syndromes. Neurology, 87(7), 709–717.  https://doi.org/10.1212/WNL.0000000000002985.CrossRefGoogle Scholar
  12. Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110(1–2), 43–50.  https://doi.org/10.1016/S0167-2789(97)00118-8
  13. 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-naïve patients with Parkinson’s disease by levodopa. Brain, 136(3), 710–725.  https://doi.org/10.1093/brain/awt007.CrossRefGoogle Scholar
  14. Fahn, S., & Elton, R. L. (1987). Unified Parkinson’s disease rating scale. In S. Fahn, C. D. Marsden, D. B. Calne, & M. Goldstein (Eds.), Recent developments in Parkinson disease (Second ed., pp. 153–163). Florham Park, NJ: Macmillan Health Care Information.Google Scholar
  15. Giménez, M., Guinea-Izquierdo, A., Villalta-Gil, V., Martínez-Zalacaín, I., Segalàs, C., Subirà, M., Real, E., Pujol, J., Harrison, B. J., Haro, J. M., Sato, J. R., Hoexter, M. Q., Cardoner, N., Alonso, P., Menchón, J. M., & Soriano-Mas, C. (2016). Brain alterations in low-frequency fluctuations across multiple bands in obsessive compulsive disorder. Brain Imaging and Behavior, 11, 1–17.  https://doi.org/10.1007/s11682-016-9601-y.Google Scholar
  16. Glerean, E., Pan, R. K., Salmi, J., Kujala, R., Lahnakoski, J. M., Roine, U., Nummenmaa, L., Leppämäki, S., Nieminen-von Wendt, T., Tani, P., Saramäki, J., Sams, M., & Jääskeläinen, I. P. (2016). Reorganization of functionally connected brain subnetworks in high-functioning autism. Human Brain Mapping, 37(3), 1066–1079.  https://doi.org/10.1002/hbm.23084.CrossRefGoogle Scholar
  17. Golbe, L. I., Davis, P. H., Schoenberg, B. S., & Duvoisin, R. C. (1988). Prevalence and natural history of progressive supranuclear palsy. Neurology, 38(7), 1031–1034 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3386818.CrossRefGoogle Scholar
  18. Gómez, C., Lizier, J. T., Schaum, M., Wollstadt, P., Grützner, C., Uhlhaas, P., Freitag, C. M., Schlitt, S., Bölte, S., Hornero, R., & Wibral, M. (2014). Reduced predictable information in brain signals in autism spectrum disorder. Frontiers in Neuroinformatics, 8(February), 1–12.  https://doi.org/10.3389/fninf.2014.00009.Google Scholar
  19. Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G., Zervakis, M., Xanthopoulos, P., Sakkalis, V., & Vanrumste, B. (2008). Review on solving the inverse problem in EEG source analysis. Journal of Neuroengineering and Rehabilitation, 5, 25.  https://doi.org/10.1186/1743-0003-5-25.CrossRefGoogle Scholar
  20. Gultepe, E., & He, B. (2013). A linear/nonlinear characterization of resting state brain networks in fMRI time series. Brain Topography, 26(1), 39–49.  https://doi.org/10.1007/S10548-012-0249-7.CrossRefGoogle Scholar
  21. Han, L., Zhaohui, L., Fei, Y., Pengfei, Z., Ting, L., Cheng, D., & Zhenchang, W. (2015). Disrupted neural activity in unilateral vascular pulsatile tinnitus patients in the early stage of disease: Evidence from resting-state fMRI. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 59, 91–99.  https://doi.org/10.1016/j.pnpbp.2015.01.013.CrossRefGoogle Scholar
  22. Harrington, D. L., Shen, Q., Castillo, G. N., Filoteo, J. V., Litvan, I., Takahashi, C., & French, C. (2017). Aberrant intrinsic activity and connectivity in cognitively Normal Parkinson?S disease. Frontiers in Aging Neuroscience, 9, 197.  https://doi.org/10.3389/fnagi.2017.00197.CrossRefGoogle Scholar
  23. Haslinger, B., Erhard, P., Kämpfe, N., Boecker, H., Rummeny, E., Schwaiger, M., et al. (2001). Event-related functional magnetic resonance imaging in Parkinson’s disease before and after levodopa. Brain : A Journal of Neurology, 124(Pt 3), 558–570 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11222456.CrossRefGoogle Scholar
  24. Hentschke, H., & Stüttgen, M. C. (2011). Computation of measures of effect size for neuroscience data sets. The European Journal of Neuroscience, 34(12), 1887–1894.  https://doi.org/10.1111/j.1460-9568.2011.07902.x.CrossRefGoogle Scholar
  25. Herz, D. M., Florin, E., Christensen, M. S., Reck, C., Barbe, M. T., Tscheuschler, M. K., et al. (2014). Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson’s disease. Cerebral Cortex, 24(11), 2873–2883.  https://doi.org/10.1093/cercor/bht140.CrossRefGoogle Scholar
  26. Hoehn, M. M., & Yahr, M. D. (n.d.). Parkinsonism: onset, progression, and mortality. Retrieved from http://www.neurology.org/content/17/5/427.full.pdf
  27. Hughes, A. J., Daniel, S. E., Kilford, L., & Lees, A. J. (1992). Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: A clinico-pathological study of 100 cases. Journal of Neurology, Neurosurgery, and Psychiatry, 55(3), 181–184 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1564476.CrossRefGoogle Scholar
  28. Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79(4), 368–376.  https://doi.org/10.1136/jnnp.2007.131045.CrossRefGoogle Scholar
  29. Kalmar, Z., Kovacs, N., Perlaki, G., Nagy, F., Aschermann, Z., Kerekes, Z., Kaszas, B., Balas, I., Orsi, G., Komoly, S., Schwarcz, A., & Janszky, J. (2011). Reorganization of motor system in Parkinson’s disease. European Neurology, 66(4), 220–226.  https://doi.org/10.1159/000330658.CrossRefGoogle Scholar
  30. Kantz, H., & Schreiber, T. (2003). Nonlinear time series analysis. Cambridge University Press. Retrieved from https://dl.acm.org/citation.cfm?id=1121581
  31. Kesić, S., & Spasić, S. Z. (2016). Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: A review. Computer Methods and Programs in Biomedicine, 133, 55–70.  https://doi.org/10.1016/J.CMPB.2016.05.014.CrossRefGoogle Scholar
  32. Kraskov, A., St, H., & Grassberger, P. (2008). Estimating mutual information, (2).Google Scholar
  33. Kwak, Y., Peltier, S. J., Bohnen, N. I., Müller, M. L. T. 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.  https://doi.org/10.3389/fnsys.2012.00052.CrossRefGoogle Scholar
  34. de Lau, L. M., & Breteler, M. M. (2006). Epidemiology of Parkinson’s disease. The Lancet Neurology, 5(6), 525–535.  https://doi.org/10.1016/S1474-4422(06)70471-9.CrossRefGoogle Scholar
  35. Lei, D., Ma, J., Du, X., Shen, G., Tian, M., & Li, G. (2012). Spontaneous brain activity changes in children with primary monosymptomatic nocturnal enuresis: A resting-state fMRI study. Neurourology and Urodynamics, 31(1), 99–104.  https://doi.org/10.1002/nau.21205.CrossRefGoogle Scholar
  36. Lindenbach, D., & Bishop, C. (2013). Critical involvement of the motor cortex in the pathophysiology and treatment of Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 37(10), 2737–2750.  https://doi.org/10.1016/j.neubiorev.2013.09.008.CrossRefGoogle Scholar
  37. Lizier, J. T. (2014). JIDT: An information-theoretic toolkit for studying the dynamics of complex systems, 1(December), 1–20.  https://doi.org/10.3389/frobt.2014.00011.
  38. Lizier, J. T., Prokopenko, M., & Zomaya, A. Y. (2012). Local measures of information storage in complex distributed computation. Information Sciences, 208, 39–54.  https://doi.org/10.1016/j.ins.2012.04.016.CrossRefGoogle Scholar
  39. Manza, P., Zhang, S., Li, C.-S. R., & Leung, H.-C. (2016). Resting-state functional connectivity of the striatum in early-stage Parkinson’s disease: Cognitive decline and motor symptomatology. Human Brain Mapping, 37(2), 648–662.  https://doi.org/10.1002/hbm.23056.CrossRefGoogle Scholar
  40. Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S., Poewe, W., Mollenhauer, B., Klinik, P. E., Sherer, T., Frasier, M., Meunier, C., Rudolph, A., Casaceli, C., Seibyl, J., Mendick, S., Schuff, N., Zhang, Y., Toga, A., Crawford, K., Ansbach, A., de Blasio, P., Piovella, M., Trojanowski, J., Shaw, L., Singleton, A., Hawkins, K., Eberling, J., Brooks, D., Russell, D., Leary, L., Factor, S., Sommerfeld, B., Hogarth, P., Pighetti, E., Williams, K., Standaert, D., Guthrie, S., Hauser, R., Delgado, H., Jankovic, J., Hunter, C., Stern, M., Tran, B., Leverenz, J., Baca, M., Frank, S., Thomas, C. A., Richard, I., Deeley, C., Rees, L., Sprenger, F., Lang, E., Shill, H., Obradov, S., Fernandez, H., Winters, A., Berg, D., Gauss, K., Galasko, D., Fontaine, D., Mari, Z., Gerstenhaber, M., Brooks, D., Malloy, S., Barone, P., Longo, K., Comery, T., Ravina, B., Grachev, I., Gallagher, K., Collins, M., Widnell, K. L., Ostrowizki, S., Fontoura, P., Ho, T., Luthman, J., Brug, M. . ., Reith, A. D., & Taylor, P. (2011). The Parkinson progression marker initiative (PPMI). Progress in Neurobiology, 95(4), 629–635.  https://doi.org/10.1016/j.pneurobio.2011.09.005.CrossRefGoogle Scholar
  41. Margulies, D. S., Böttger, J., Long, X., Lv, Y., Kelly, C., Schäfer, A., et al. (2010). Resting developments: A review of fMRI post-processing methodologies for spontaneous brain activity. Magma (New York, N.Y.), 23(5–6), 289–307.  https://doi.org/10.1007/s10334-010-0228-5.
  42. Meda, S. A., Wang, Z., Ivleva, E. I., Poudyal, G., Keshavan, M. S., Tamminga, C. A., Sweeney, J. A., Clementz, B. A., Schretlen, D. J., Calhoun, V. D., Lui, S., Damaraju, E., & Pearlson, G. D. (2015). Frequency-specific neural signatures of spontaneous low-frequency resting state fluctuations in psychosis: Evidence from bipolar-schizophrenia network on intermediate phenotypes (B-SNIP) consortium. Schizophrenia Bulletin, 41(6), 1336–1348.  https://doi.org/10.1093/schbul/sbv064.CrossRefGoogle Scholar
  43. Michely, J., Volz, L. J., Barbe, M. T., Hoffstaedter, F., Viswanathan, S., Timmermann, L., Eickhoff, S. B., Fink, G. R., & Grefkes, C. (2015). Dopaminergic modulation of motor network dynamics in Parkinson’s disease. Brain, 138(3), 664–678.  https://doi.org/10.1093/brain/awu381.CrossRefGoogle Scholar
  44. Miller, D. B., & O’Callaghan, J. P. (2015). Biomarkers of Parkinson’s disease: Present and future. Metabolism, 64(3), S40–S46.  https://doi.org/10.1016/j.metabol.2014.10.030.CrossRefGoogle Scholar
  45. Montgomery, E. B. (2016). Modeling and theories of pathophysiology and physiology of the basal ganglia-thalamic-cortical system: Critical analysis. Frontiers in Human Neuroscience, 10, 469.  https://doi.org/10.3389/fnhum.2016.00469.CrossRefGoogle Scholar
  46. Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., et al. (2005). The Montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699.  https://doi.org/10.1111/j.1532-5415.2005.53221.x.CrossRefGoogle Scholar
  47. Ng, B., Varoquaux, G., Poline, J. B., Thirion, B., Greicius, M. D., & Poston, K. L. (2017). Distinct alterations in Parkinson’s medication-state and disease-state connectivity. NeuroImage: Clinical, 16, 575–585.  https://doi.org/10.1016/j.nicl.2017.09.004.CrossRefGoogle Scholar
  48. Obeso, J. A., Rodriguez-Oroz, M. C., Stamelou, M., Bhatia, K. P., & Burn, D. J. (2014). The expanding universe of disorders of the basal ganglia. The Lancet, 384(9942), 523–531.  https://doi.org/10.1016/S0140-6736(13)62418-6.CrossRefGoogle Scholar
  49. Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of America, 89(13), 5951–5955 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1631079.CrossRefGoogle Scholar
  50. Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., Schrag, A. E., & Lang, A. E. (2017). Parkinson disease. Nature Reviews Disease Primers, 3, 17013.  https://doi.org/10.1038/nrdp.2017.13.CrossRefGoogle Scholar
  51. Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., Obeso, J., Marek, K., Litvan, I., Lang, A. E., Halliday, G., Goetz, C. G., Gasser, T., Dubois, B., Chan, P., Bloem, B. R., Adler, C. H., & Deuschl, G. (2015). MDS clinical diagnostic criteria for Parkinson’s disease. Movement Disorders : Official Journal of the Movement Disorder Society, 30(12), 1591–1601.  https://doi.org/10.1002/mds.26424.CrossRefGoogle Scholar
  52. Premi, E., Cauda, F., Gasparotti, R., Diano, M., Archetti, S., Padovani, A., & Borroni, B. (2014). Multimodal fMRI resting-state functional connectivity in Granulin mutations: The case of Fronto-parietal dementia. PLoS One, 9(9), e106500.  https://doi.org/10.1371/journal.pone.0106500.CrossRefGoogle Scholar
  53. Pringsheim, T., Wiltshire, K., Day, L., Dykeman, J., Steeves, T., & Jette, N. (2012). The incidence and prevalence of Huntington’s disease: A systematic review and meta-analysis. Movement Disorders, 27(9), 1083–1091.  https://doi.org/10.1002/mds.25075.CrossRefGoogle Scholar
  54. Proal, E., Alvarez-Segura, M., de la Iglesia-Vayá, M., Martí-Bonmatí, L., Castellanos, F. X., & Spanish resting state network. (2011). [Functional cerebral activity in a state of rest: Connectivity networks]. Revista de Neurologia, 52 Suppl 1, S3‑10. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21365601.
  55. Rektorova, I. (2013). Resting-state networks in Alzheimer’s disease and Parkinson’s disease. Neurodegenerative Diseases, 13(2–3), 186–188.  https://doi.org/10.1159/000354237.CrossRefGoogle Scholar
  56. Rodriguez-Oroz, M. C., Jahanshahi, M., Krack, P., Litvan, I., Macias, R., Bezard, E., & Obeso, J. A. (2009). Initial clinical manifestations of Parkinson’s disease: Features and pathophysiological mechanisms. The Lancet Neurology, 8(12), 1128–1139.  https://doi.org/10.1016/S1474-4422(09)70293-5.CrossRefGoogle Scholar
  57. Sabatini, U., Boulanouar, K., Fabre, N., Martin, F., Carel, C., Colonnese, C., et al. (2000). Cortical motor reorganization in akinetic patients with Parkinson’s disease: a functional MRI study. Brain : A Journal of Neurology, 123(Pt 2), 394–403 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10648446.CrossRefGoogle Scholar
  58. Schrag, A., Ben-Shlomo, Y., & Quinn, N. P. (1999). Prevalence of progressive supranuclear palsy and multiple system atrophy: A cross-sectional study. Lancet (London, England), 354(9192), 1771–1775 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10577638.CrossRefGoogle Scholar
  59. Segall, J. M., Allen, E. A., Jung, R. E., Erhardt, E. B., Arja, S. K., Kiehl, K., & Calhoun, V. D. (2012). Correspondence between structure and function in the human brain at rest. Frontiers in Neuroinformatics, 6, 10.  https://doi.org/10.3389/fninf.2012.00010.CrossRefGoogle Scholar
  60. Sepulcre, J., Sabuncu, M. R., & Johnson, K. A. (2012). Network assemblies in the functional brain. Current Opinion in Neurology, 25(4), 1.  https://doi.org/10.1097/WCO.0b013e328355a8e8.CrossRefGoogle Scholar
  61. Sharman, M., Valabregue, R., Perlbarg, V., Marrakchi-Kacem, L., Vidailhet, M., Benali, H., Brice, A., & Lehéricy, S. (2013). Parkinson’s disease patients show reduced cortical-subcortical sensorimotor connectivity. Movement Disorders, 28(4), 447–454.  https://doi.org/10.1002/mds.25255.CrossRefGoogle Scholar
  62. Smith, Y. (2012). Functional anatomy of the basal ganglia. In R. L. Watts, D. G. Standaert, & J. A. Obeso (Eds.), Movement Disorders (Third ed.). New York: McGraw Hill Professional.Google Scholar
  63. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045.  https://doi.org/10.1073/pnas.0905267106.CrossRefGoogle Scholar
  64. Sokunbi, M. O. (2016). BOLD fMRI complexity predicts changes in brain processes, interactions and patterns, in health and disease. Journal of the Neurological Sciences, 367(June), 347–348.  https://doi.org/10.1016/j.jns.2016.06.040.CrossRefGoogle Scholar
  65. Tahmasian, M., Bettray, L. M., van Eimeren, T., Drzezga, A., Timmermann, L., Eickhoff, C. R., Eickhoff, S. B., & Eggers, C. (2015). A systematic review on the applications of resting-state fMRI in Parkinson’s disease: Does dopamine replacement therapy play a role? Cortex, 73, 80–105.  https://doi.org/10.1016/j.cortex.2015.08.005.CrossRefGoogle Scholar
  66. Takens, F. (1981). Detecting strange attractors in turbulence. In D. Rand & YoungL (Eds.), Dynamical Systems and Turbulence (pp. 366–381). Springer-Verlag.Google Scholar
  67. Tessitore, A., Giordano, A., De Micco, R., Russo, A., & Tedeschi, G. (2014). Sensorimotor connectivity in Parkinson’s disease: The role of functional neuroimaging. Frontiers in Neurology, 5, 180.  https://doi.org/10.3389/fneur.2014.00180.CrossRefGoogle Scholar
  68. Togasaki, D. M., & Tanner, C. M. (2000). Epidemiologic aspects. Advances in Neurology, 82, 53–59 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10624470.Google Scholar
  69. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical Parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289.  https://doi.org/10.1006/nimg.2001.0978.CrossRefGoogle Scholar
  70. Uhlhaas, P. J., & Singer, W. (2006). Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology. Neuron, 52(1), 155–168.  https://doi.org/10.1016/j.neuron.2006.09.020.CrossRefGoogle Scholar
  71. Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229–239.  https://doi.org/10.1038/35067550.CrossRefGoogle Scholar
  72. Wang, J., Zhang, J.-R., Zang, Y.-F., & Wu, T. (2018). Consistent decreased activity in the putamen in Parkinson’s disease: A meta-analysis and an independent validation of resting-state fMRI. GigaScience, 7(6).  https://doi.org/10.1093/gigascience/giy071.
  73. Wollstadt, P., Sellers, K. K., Hutt, A., Frohlich, F., & Wibral, M. (2015). Anesthesia-related changes in information transfer may be caused by reduction in local information generation. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Vol. 2015, pp. 4045–4048). IEEE.  https://doi.org/10.1109/EMBC.2015.7319282.
  74. Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in System Neuroscience, 4, 13.  https://doi.org/10.3389/fnsys.2010.00013.Google Scholar
  75. Yang, H., Zhou, X. J., Zhang, M., Zheng, X., Zhao, Y., & Wang, J. (2013). Changes in spontaneous brain activity in early Parkinson’s disease. Neuroscience Letters, 549, 24–28.  https://doi.org/10.1016/j.neulet.2013.05.080.CrossRefGoogle Scholar
  76. Yoo, K., Chung, S. J., Kim, H. S., Choung, O., Lee, Y.-B., Kim, M.-J., You, S., & Jeong, Y. (2015). Neural substrates of motor and non-motor symptoms in Parkinson’s disease: A resting fMRI study. PLoS One, 10(4), e0125455.  https://doi.org/10.1371/journal.pone.0125455.CrossRefGoogle Scholar
  77. Yu, H., Sternad, D., Corcos, D. M., & Vaillancourt, D. E. (2007). Role of hyperactive cerebellum and motor cortex in Parkinson’s disease. NeuroImage, 35(1), 222–233.  https://doi.org/10.1016/j.neuroimage.2006.11.047.CrossRefGoogle Scholar
  78. Zang, Y.-F., Zuo, X.-N., Milham, M., & Hallett, M. (2015). Toward a meta-analytic synthesis of the resting-state fMRI literature for clinical populations. BioMed Research International, 2015, 435265–435263.  https://doi.org/10.1155/2015/435265.CrossRefGoogle Scholar
  79. Zhou, F., Zhuang, Y., Gong, H., Zhan, J., Grossman, M., & Wang, Z. (2016). Resting state brain entropy alterations in relapsing remitting multiple sclerosis. PLoS One, 11(1), 146080.  https://doi.org/10.1371/journal.pone.0146080.Google Scholar
  80. Zou, Q.-H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J., Wang, Y. F., & Zang, Y.-F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172). Retrieved from http://www.sciencedirect.com/science/article/pii/S0165027008002458, 137–141.CrossRefGoogle Scholar
  81. Zuo, X.-N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., et al. (2010). The oscillating brain: Complex and reliable. NeuroImage, 49(2), 1432–1445.  https://doi.org/10.1016/j.neuroimage.2009.09.037.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Grupo de Investigación en Bioinstrumentación e Ingeniería Clínica, Facultad de IngenieríaUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Grupo de Neurociencias de Antioquia, Facultad de MedicinaUniversidad de Antioquia UdeAMedellínColombia
  3. 3.Grupo Neuropsicología y Conducta, Facultad de MedicinaUniversidad de Antioquia UdeAMedellínColombia

Personalised recommendations