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Connectivity Changes in Parkinson’s Disease

  • Neuroimaging (DJ Brooks, Section Editor)
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Abstract

Parkinson’s disease (PD) is a chronic and progressive movement disorder of the central nervous system characterized by widespread alterations in several non-motor aspects such as mood, sleep, olfactory, and cognition in addition to motor dysfunctions. Advanced neuroimaging using functional connectivity reconstruction of the human brain has provided a vast knowledge on the pathophysiological mechanisms underlying this disorder, but this, however, does not cover the overall inter-/intra-individual variability of PD phenotypes. The present review is aimed at discussing to what extent the evidence provided by group-based neuroimaging analysis in this field of study (using seed-based, network-based, or graph theory approaches) may be generalized. In particular, we summarized the literature on the application of resting-state functional connectivity studies to explore different neural correlates of motor and non-motor symptoms of PD and the neural mechanisms involved in treatment effects: effects of levodopa or deep brain stimulation. The lesson learnt from one decade of studies provides consistent evidence on the role of the altered communication between the striato-frontal pathways as a marker of PD-related motor degeneration, whereas in the non-motor domain, several missing pieces of a complex puzzle are provided. However, the main target is to present a new era of intelligent neuroimaging applications, where automated multivariate analysis of functional connectivity data may be used for moving from group-level statistical results to personalized predictions in a clinical setting. Although in its relative infancy, the evidence gathered so far suggests a new era of clinical neuroimaging is starting.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Hassler R. Zur pathologie der paralysis agitans und des postenzephalitischen parkinsonismus. J Psychol Neurol. 1938;48:387–476.

    Google Scholar 

  2. Goedert M, Spillantini MG, Del Tredici K, Braak H. 100 years of Lewy pathology. Nat Rev Neurol. 2013;9:13–24.

    Article  CAS  PubMed  Google Scholar 

  3. Langston JW. The Parkinson’s complex: parkinsonism is just the tip of the iceberg. Ann Neurol. 2006;59:591–6.

    Article  PubMed  Google Scholar 

  4. Smith Y, Wichmann T, Factor SA, DeLong MR. Parkinson’s disease therapeutics: new developments and challenges since the introduction of levodopa. Neuropsychopharmacology. 2012;37:213–46.

    Article  CAS  PubMed  Google Scholar 

  5. Sulzer D, Surmeier DJ. Neuronal vulnerability, pathogenesis, and Parkinson’s disease. Mov Disord. 2013;28:41–50.

    Article  CAS  PubMed  Google Scholar 

  6. Stoessl AJ, Lehericy S, Strafella AP. Imaging insights into basal ganglia function, Parkinson’s disease, and dystonia. Lancet. 2014;384:532–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Prodoehl J, Burciu RG, Vaillancourt DE. Resting state functional magnetic resonance imaging in Parkinson’s disease. Curr Neurol Neurosci Rep. 2014;14:448.

    Article  PubMed  Google Scholar 

  8. Stoessl AJ, Martin WRW, McKeown MJ, Sossi V. Advances in imaging in Parkinson’s disease. Lancet Neurol. 2011;10:987–1001.

    Article  PubMed  Google Scholar 

  9. Tuite PJ, Mangia S, Michaeli S. Magnetic resonance imaging (MRI) in Parkinson’s disease. J Alzheimer’s Dis Park. 2013;Suppl 1:1.

    Google Scholar 

  10. Pyatigorskaya N, Gallea C, Garcia-Lorenzo D, Vidailhet M, Lehericy S. A review of the use of magnetic resonance imaging in Parkinson’s disease. Ther Adv Neurol Disord. 2013;7:206–20.

    Article  Google Scholar 

  11. Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP. Clinical applications of the functional connectome. Neuroimage. 2013;80:527–40.

    Article  CAS  PubMed  Google Scholar 

  12. Zuo XN, Xing XX. Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neurosci Biobehav Rev Elsevier Ltd. 2014;45:100–18.

    Article  Google Scholar 

  13. Raichle ME. Neuroscience. The brain’s dark energy. Science. 2006;314:1249–50.

    Article  CAS  PubMed  Google Scholar 

  14. Raichle ME, Mintun MA. Brain work and brain imaging. Annu Rev Neurosci. 2006;29:449–76.

    Article  CAS  PubMed  Google Scholar 

  15. Pinter D, Beckmann C, Koini M, Pirker E, Filippini N, Pichler A, et al. Reproducibility of resting state connectivity in patients with stable multiple sclerosis. PLoS One. 2016;11:e0152158.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Drago V, Babiloni C, Bartrés-Faz D, Caroli A, Bosch B, Hensch T, et al. Disease tracking markers for Alzheimer’s disease at the prodromal (MCI) stage. J Alzheimers Dis. 2011;26 Suppl 3:159–99.

    PubMed  Google Scholar 

  17. Tahmasian M, Bettray LM, van Eimeren T, Drzezga A, Timmermann L, Eickhoff CR, et al. A systematic review on the applications of resting-state fMRI in Parkinson’s disease: does dopamine replacement therapy play a role? Cortex. 2015;73:80–105.

    Article  PubMed  Google Scholar 

  18. Borgwardt S, Fusar-Poli P. Third-generation neuroimaging in early schizophrenia: translating research evidence into clinical utility. Br J Psychiatry. 2012;200:270–2.

    Article  PubMed  Google Scholar 

  19. Weingarten CP, Sundman MH, Hickey P, Chen N. Neuroimaging of Parkinson’s disease: expanding views. Neurosci Biobehav Rev. 2015;59:16–52.

    Article  PubMed  Google Scholar 

  20. Bishop CM. Pattern recognition and machine learning. In: Jordan M, Kleinberg J, Scholkopf B, editors. Springer; 2006.

  21. Noirhomme Q, Brecheisen R, Lesenfants D, Antonopoulos G, Laureys S. “Look at my classifier’s result”: disentangling unresponsive from (minimally) conscious patients. Neuroimage. 2015. doi:10.1016/j.neuroimage.2015.12.006.

    PubMed  Google Scholar 

  22. Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16:933–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36:1140–52.

    Article  PubMed  Google Scholar 

  24. Jankovic J. Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008;79:368–76.

    Article  CAS  PubMed  Google Scholar 

  25. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8:700–11.

    Article  CAS  PubMed  Google Scholar 

  26. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. p. 2322–45.

  27. Raichle ME. The restless brain. Brain Connect. 2011;1:3–12.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Van Dijk KRA, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol. 2010;103:297–321.

    Article  PubMed  Google Scholar 

  29. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–41.

    Article  CAS  PubMed  Google Scholar 

  30. Ouchi Y, Kikuchi M. A review of the default mode network in aging and dementia based on molecular imaging. Rev Neurosci. 2012;23:263–8.

    CAS  PubMed  Google Scholar 

  31. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20:519–34.

    Article  PubMed  Google Scholar 

  32. Hacker CD, Perlmutter JS, Criswell SR, Ances BM, Snyder AZ. Resting state functional connectivity of the striatum in Parkinson’s disease. Brain. 2012;135:3699–711.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Helmich RC, Derikx LC, Bakker M, Scheeringa R, Bloem BR, Toni I. Spatial remapping of cortico-striatal connectivity in Parkinson’s disease. Cereb Cortex. 2010;20:1175–86.

    Article  PubMed  Google Scholar 

  34. Yu R, Liu B, Wang L, Chen J, Liu X. Enhanced functional connectivity between putamen and supplementary motor area in Parkinson’s disease patients. PLoS One. 2013;8:e59717.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Baudrexel S, Witte T, Seifried C, von Wegner F, Beissner F, Klein JC, et al. Resting state fMRI reveals increased subthalamic nucleus-motor cortex connectivity in Parkinson’s disease. Neuroimage. 2011;55:1728–38.

    Article  PubMed  Google Scholar 

  36. Kurani AS, Seidler RD, Burciu RG, Comella CL, Corcos DM, Okun MS, et al. Subthalamic nucleus-sensorimotor cortex functional connectivity in de novo and moderate Parkinson’s disease. Neurobiol Aging. 2015;36:462–9.

    Article  PubMed  Google Scholar 

  37. Sharman M, Valabregue R, Perlbarg V, Marrakchi-Kacem L, Vidailhet M, Benali H, et al. Parkinson’s disease patients show reduced cortical-subcortical sensorimotor connectivity. Mov Disord. 2013;28:447–54.

    Article  CAS  PubMed  Google Scholar 

  38. Wu T, Long X, Wang L, Hallett M, Zang Y, Li K, et al. Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease. Hum Brain Mapp. 2011;32:1443–57.

    Article  PubMed  Google Scholar 

  39. Helmich RC, Janssen MJR, Oyen WJG, Bloem BR, Toni I. Pallidal dysfunction drives a cerebellothalamic circuit into Parkinson tremor. Ann Neurol. 2011;69:269–81. In this study, Helmich and colleagues provide a complete neurophysiological and neurobiological picture of pathophysiological mechanisms underlying resting tremor in Parkinson’s disease (PD). They investigate functional connectivity between basal ganglia nuclei and cerebellothalamic circuit using resting-state fMRI, the severity of tremor using electromyographic evaluation, and striatal dopamine depletion with PET imaging. They found that (a) the activity in the cerebellum-thalamo-motor cortex network cofluctuated with tremor amplitude in PD patients, (b) pallidal dopamine depletion correlated with clinical tremor severity, and (c) in tremor-dominant PD, the most-affected pallidum showed increased functional connectivity with the motor cortex node.

    Article  PubMed  Google Scholar 

  40. Fling BW, Cohen RG, Mancini M, Carpenter SD, Fair DA, Nutt JG, et al. Functional reorganization of the locomotor network in Parkinson patients with freezing of gait. PLoS One. 2014;9:e100291.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tessitore A, Amboni M, Esposito F, Russo A, Picillo M, Marcuccio L, et al. Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait. Parkinsonism Relat Disord. 2012;18:781–7. Authors investigated 16 Parkinson’s disease (PD) patients with freezing of gait (FOG), 13 PD without FOG, and 15 controls. They found that disruption of connectivity within the executive-attention and visual networks may be associated with the development of FOG.

    Article  PubMed  Google Scholar 

  42. Jenner P. Functional models of Parkinson’s disease: a valuable tool in the development of novel therapies. Ann Neurol. 2008;64:16–29.

    Article  Google Scholar 

  43. Müller-Oehring EM, Sullivan EV, Pfefferbaum A, Huang NC, Poston KL, Bronte-Stewart HM, et al. Task-rest modulation of basal ganglia connectivity in mild to moderate Parkinson’s disease. Brain Imaging Behav. 2015;9:619–38.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Olde Dubbelink KTE, Schoonheim MM, Deijen JB, Twisk JWR, Barkhof F, Berendse HW. Functional connectivity and cognitive decline over 3 years in Parkinson disease. Neurology. 2014;83:2046–53. This is one of the few neuroimaging studies aimed at measuring the clinical disease progression of Parkinson’s disease (PD) patients. They investigated 36 PD patients in a 3-year follow-up period. In the baseline, PD patients are characterized by widespread changes in several brain networks with respect to controls. After 3 years, decreasing in functional connectivity progressively worsens, and it was associated with clinical deterioration, especially with cognitive decline. In particular, the posterior parts of the brain (i.e., precuneus, parietal cortex) showed reduction of functional connectivity. This follow-up study confirms that resting-state functional connectivity is an important hallmark of PD-related cognitive decline.

    Article  CAS  PubMed  Google Scholar 

  45. Dirnberger G, Jahanshahi M. Executive dysfunction in Parkinson’s disease: a review. J Neuropsychol. 2013;7:193–224.

    Article  PubMed  Google Scholar 

  46. Rektorova I, Krajcovicova L, Marecek R, Mikl M. Default mode network and extrastriate visual resting state network in patients with Parkinson’s disease dementia. Neurodegener Dis. 2012;10:232–7.

    Article  CAS  PubMed  Google Scholar 

  47. Vandekerckhove M, Panksepp J. A neurocognitive theory of higher mental emergence: from anoetic affective experiences to noetic knowledge and autonoetic awareness. Neurosci Biobehav Rev. 2011;35:2017–25.

    Article  PubMed  Google Scholar 

  48. Tessitore A, Esposito F, Vitale C, Santangelo G, Amboni M, Russo A, et al. Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology. 2012;79:2226–32.

    Article  PubMed  Google Scholar 

  49. Amboni M, Tessitore A, Esposito F, Santangelo G, Picillo M, Vitale C, et al. Resting-state functional connectivity associated with mild cognitive impairment in Parkinson’s disease. J Neurol. 2014;262:425–34.

    Article  PubMed  Google Scholar 

  50. Disbrow EA, Carmichael O, He J, Lanni KE, Dressler EM, Zhang L, et al. Resting state functional connectivity is associated with cognitive dysfunction in non-demented people with Parkinson’s disease. J Parkinson’s Dis. 2014;4:453–65.

    CAS  Google Scholar 

  51. Krajcovicova L, Mikl M, Marecek R, Rektorova I. The default mode network integrity in patients with Parkinson’s disease is levodopa equivalent dose-dependent. J Neural Transm. 2012;119:443–54.

    Article  CAS  PubMed  Google Scholar 

  52. Cerasa A, Gioia MC, Salsone M, Donzuso G, Chiriaco C, Realmuto S, et al. Neurofunctional correlates of attention rehabilitation in Parkinson’s disease: an explorative study. Neurol Sci. 2014;35:1173–80. In this neuroimaging study, we demonstrated that the activity of resting-state functional connectivity might be used as hallmark for evaluating the effectiveness of cognitive rehabilitation (CR). Using a randomized controlled study, a group of Parkinson’s disease (PD) patients underwent CR program tailored for attention abilities, while others PD underwent a placebo intervention. CR had beneficial effects on executive functions, which was sustained by underlying improved activity in the attention and central executive neural networks.

    Article  PubMed  Google Scholar 

  53. Luo C, Guo X, Song W, Chen Q, Yang J, Gong Q, Shang HF. The trajectory of disturbed resting-state cerebral function in Parkinson's disease at different Hoehn and Yahr stages. Hum Brain Mapp. 2015;36:3104–16.

  54. Luo C, Chen Q, Song W, Chen K, Guo X, Yang J, et al. Resting-state fMRI study on drug-naive patients with Parkinson’s disease and with depression. J Neurol Neurosurg Psychiatry. 2014;4:675–83.

    Article  Google Scholar 

  55. Sheng K, Fang W, Su M, Li R, Zou D, Han Y, et al. Altered spontaneous brain activity in patients with Parkinson’s disease accompanied by depressive symptoms, as revealed by regional homogeneity and functional connectivity in the prefrontal-limbic system. PLoS One. 2014;9:e84705.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Shine JM, Halliday GM, Gilat M, Matar E, Bolitho SJ, Carlos M, et al. The role of dysfunctional attentional control networks in visual misperceptions in Parkinson’s disease. Hum Brain Mapp. 2014;35:2206–19.

    Article  PubMed  Google Scholar 

  57. Yao N, Shek-Kwan Chang R, Cheung C, Pang S, Lau KK, Suckling J, et al. The default mode network is disrupted in Parkinson’s disease with visual hallucinations. Hum Brain Mapp. 2014;35:5658–66.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ellmore TM, Castriotta RJ, Hendley KL, Aalbers BM, Furr-Stimming E, Hood AJ, et al. Altered nigrostriatal and nigrocortical functional connectivity in rapid eye movement sleep behavior disorder. Sleep. 2013;36:1885–92.

    PubMed  PubMed Central  Google Scholar 

  59. Wu T, Long X, Zang Y, Wang L, Hallett M, Li K, et al. Regional homogeneity changes in patients with parkinson’s disease. Hum Brain Mapp. 2009;30:1502–10.

    Article  PubMed  Google Scholar 

  60. Kwak Y, Peltier SJ, Bohnen NI, Müller MLTM, Dayalu P, Seidler RD. L-DOPA changes spontaneous low-frequency BOLD signal oscillations in Parkinson’s disease: a resting state fMRI study. Front Syst Neurosci. 2012;6:52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Esposito F, Tessitore A, Giordano A, De Micco R, Paccone A, Conforti R, et al. Rhythm-specific modulation of the sensorimotor network in drug-naive patients with Parkinson’s disease by levodopa. Brain. 2013;136:710–25.

    Article  PubMed  Google Scholar 

  62. Cerasa A, Koch G, Donzuso G, Mangone G, Morelli M, Brusa L, et al. A network centred on the inferior frontal cortex is critically involved in levodopa-induced dyskinesias. Brain. 2015;138:414–27.

    Article  PubMed  Google Scholar 

  63. Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci. 2014;18:177–85.

    Article  PubMed  Google Scholar 

  64. Kahan J, Urner M, Moran R, Flandin G, Marreiros A, Mancini L, et al. Resting state functional MRI in Parkinson’s disease: the impact of deep brain stimulation on “effective” connectivity. Brain. 2014;137:1130–44.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Cerasa A. Machine learning on Parkinson’s disease? Let’s translate into clinical practice. J Neurosci Methods. 2016;266:161–2.

    Article  PubMed  Google Scholar 

  66. Cherubini A, Nisticó R, Novellino F, Salsone M, Nigro S, Donzuso G, et al. Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease. Mov Disord. 2014;29:1216–9.

    Article  PubMed  Google Scholar 

  67. Herz DM, Haagensen BN, Nielsen SH, Madsen KH, Løkkegaard A, Siebner HR. Resting-state connectivity predicts levodopa-induced dyskinesias in Parkinson’s disease. Mov Disord. 2016;31:521–9. The Siebner’s group demonstrated that resting-state activity might be used for diagnostic and prognostic purposes. Indeed, using machine-learning algorithm, they automatically classified PD patients with and without levodopa-induced dyskinesias (LID) only using information from resting-state fMRI sequence. In particular, the altered connectivity between the putamen with the sensorimotor cortex allowed to perform the best discrimination (specificity 100%; sensitivity 91%) between PD with and without LID. Whereas, modulation of resting-state connectivity between the supplementary motor area and putamen predicted interindividual differences in dyskinesia severity.

    Article  CAS  PubMed  Google Scholar 

  68. Long D, Wang J, Xuan M, Gu Q, Xu X, Kong D, et al. Automatic classification of early Parkinson’s disease with multi-modal MR imaging. PLoS One. 2012;7:1–9.

    Google Scholar 

  69. Chen Y, Yang W, Long J, Zhang Y, Feng J, Li Y, et al. Discriminative analysis of Parkinson’s disease based on whole-brain functional connectivity. PLoS One. 2015;10:1–16.

    Google Scholar 

  70. Zhang D, Liu X, Chen J, Liu B. Distinguishing patients with Parkinson’s disease subtypes from normal controls based on functional network regional efficiencies. PLoS One. 2014;9:e115131.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Wu T, Ma Y, Zheng Z, Peng S, Wu X, Eidelberg D, et al. Parkinson’s disease-related spatial covariance pattern identified with resting-state functional MRI. J Cereb Blood Flow Metab. 2015;1:1–7.

    Google Scholar 

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    Authors and Affiliations

    1. IBFM, National Research Council, Germaneto, CZ, Italy

      Antonio Cerasa, Fabiana Novellino & Aldo Quattrone

    2. Institute of Neurology, Department of Medical Sciences, University Magna Graecia Catanzaro, Catanzaro, Italy

      Aldo Quattrone

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    1. Antonio Cerasa
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    Correspondence to Aldo Quattrone.

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    This article is part of the Topical Collection on Neuroimaging

    Antonio Cerasa and Fabiana Novellino contributed equally to this work.

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    Cerasa, A., Novellino, F. & Quattrone, A. Connectivity Changes in Parkinson’s Disease. Curr Neurol Neurosci Rep 16, 91 (2016). https://doi.org/10.1007/s11910-016-0687-9

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