The Effect of Neuroepo on Cognition in Parkinson's Disease Patients Is Mediated by Electroencephalogram Source Activity

Maria L Bringas Vega, Ivonne Pedroso Ibáñez, Fuleah A Razzaq, Min Zhang, Lilia Morales Chacón, Peng Ren, Lidice Galan Garcia, Peng Gan, Trinidad Virues Alba, Carlos Lopez Naranjo, Marjan Jahanshahi, Jorge Bosch-Bayard, Pedro A Valdes-Sosa, Maria L Bringas Vega, Ivonne Pedroso Ibáñez, Fuleah A Razzaq, Min Zhang, Lilia Morales Chacón, Peng Ren, Lidice Galan Garcia, Peng Gan, Trinidad Virues Alba, Carlos Lopez Naranjo, Marjan Jahanshahi, Jorge Bosch-Bayard, Pedro A Valdes-Sosa

Abstract

We report on the quantitative electroencephalogram (qEEG) and cognitive effects of Neuroepo in Parkinson's disease (PD) from a double-blind safety trial (https://ichgcp.net/clinical-trials-registry/NCT04110678" title="See in ClinicalTrials.gov">NCT04110678). Neuroepo is a new erythropoietin (EPO) formulation with a low sialic acid content with satisfactory results in animal models and tolerance in healthy participants and PD patients. In this study, 26 PD patients were assigned randomly to Neuroepo (n = 15) or placebo (n = 11) groups to test the tolerance of the drug. Outcome variables were neuropsychological tests and resting-state source qEEG at baseline and 6 months after administering the drug. Probabilistic Canonical Correlation Analysis was used to extract latent variables for the cognitive and for qEEG variables that shared a common source of variance. We obtained canonical variates for Cognition and qEEG with a correlation of 0.97. Linear Mixed Model analysis showed significant positive dependence of the canonical variate cognition on the dose and the confounder educational level (p = 0.003 and p = 0.02, respectively). Additionally, in the mediation equation, we found a positive dependence of Cognition with qEEG for (p = < 0.0001) and with dose (p = 0.006). Despite the small sample, both tests were powered over 89%. A combined mediation model showed that 66% of the total effect of the cognitive improvement was mediated by qEEG (p = 0.0001), with the remaining direct effect between dose and Cognition (p = 0.002), due to other causes. These results suggest that Neuroepo has a positive influence on Cognition in PD patients and that a large portion of this effect is mediated by brain mechanisms reflected in qEEG.

Keywords: Canonical Correlation Analysis (CCA); EEG; Neuroepo; Parkinson’s disease; source analysis; whitening.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Bringas Vega, Pedroso Ibáñez, Razzaq, Zhang, Morales Chacón, Ren, Galan Garcia, Gan, Virues Alba, Lopez Naranjo, Jahanshahi, Bosch-Bayard and Valdes-Sosa.

Figures

FIGURE 1
FIGURE 1
A mediation model. Effect of dosage on Cognition via qEEG. All three are repeated measures at two-time points. Ovals are latent variables, whereas rectangle shows observed variables. In this causal diagram, path c is the “direct effect of the Dose on Cognition.” Path following links a and b represent the “mediation effect” we wish to test.
FIGURE 2
FIGURE 2
Frequency marginal distribution of qEEG loading. Minimum and maximum loadings of WqEEGfor 3,244 sources at each frequency point. The y-axis is pCCA loadings for scaled data, and the x-axis is the frequency in Hz. Here we are using the classic frequency bands: delta (δ) = 1–4 Hz, theta (θ) = 4–8 Hz, alpha (α) = 8–12.5 Hz, and beta (β) = 12.5–20 Hz.
FIGURE 3
FIGURE 3
Topographic maps of the sources corresponding to the loadings of the qEEG latent variable for each classical frequency bands.
FIGURE 4
FIGURE 4
The mediation effect. The indirect path between dose and Cog via qEEG shows a more robust and higher estimate than the direct effect. The black diamond symbol shows the point estimate, and the line shows the 95% quasi-Bayesian confidence intervals. The mediation effect explains 66% of the total effect. There is also a significant direct effect between doses and Cog, which is not explained by qEEG. A positive mediation and direct effect show that a higher dosage is associated with higher latent cognitive scores.

References

    1. Aarsland D., Batzu L., Halliday G. M., Geurtsen G. J., Ballard C., Ray Chaudhuri K., et al. (2021). Parkinson disease-associated cognitive impairment. Nat. Rev. Dis. Prim. 7:47. 10.1038/s41572-021-00280-3
    1. Arnaldi D., De Carli F., Famà F., Brugnolo A., Girtler N., Picco A., et al. (2017). Prediction of cognitive worsening in de novo Parkinson’s disease: clinical use of biomarkers. Mov. Disord. 32 1738–1747. 10.1002/mds.27190
    1. Athauda D., Foltynie T. (2015). The ongoing pursuit of neuroprotective therapies in Parkinson disease. Nat. Rev. Neurol. 11 25–40. 10.1038/nrneurol.2014.226
    1. Babiloni C., Del Percio C., Lizio R., Noce G., Cordone S., Lopez S., et al. (2017). Abnormalities of cortical neural synchronization mechanisms in subjects with mild cognitive impairment due to Alzheimer’s and Parkinson’s diseases: an EEG study. J. Alzheimers Dis. 59 339–358. 10.3233/JAD-160883
    1. Bates D., Mächler M., Bolker B., Walker S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67 1–48. 10.18637/jss.v067.i01
    1. Bean J. (2011). “Rey auditory verbal learning test, rey AVLT,” in Encyclopedia of Clinical Neuropsychology, eds Kreutzer J. S., DeLuca J., Caplan B. (New York, NY: Springer New York; ), 2174–2175. 10.1007/978-0-387-79948-3_1153
    1. Betrouni N., Delval A., Chaton L., Defebvre L., Duits A., Moonen A., et al. (2019). Electroencephalography-based machine learning for cognitive profiling in Parkinson’s disease: preliminary results. Mov. Disord. 34 210–217. 10.1002/mds.27528
    1. Bosch-Bayard J., Aubert-Vázquez E., Brown S., Rogers C., Kiar G., Glatard T. (2020). A quantitative EEG toolbox for the MNI Neuroinformatics ecosystem: normative SPM of EEG source spectra. Front. Neuroinform. 14:33. 10.3389/fninf.2020.00033
    1. Bosch-Bayard J., Valdés-Sosa P., Virues-Alba T., Aubert-Vázquez E., John E. R., Harmony T., et al. (2001). 3D statistical parametric mapping of EEG source spectra by means of variable resolution electromagnetic tomography (VARETA). Clin. Electroencephalogr. 32 47–61. 10.1177/155005940103200203
    1. Bringas Vega M. L., Guo Y., Tang Q., Razzaq F. A., Calzada Reyes A., Ren P., et al. (2019). An age-adjusted EEG source classifier accurately detects school-aged barbadian children that had protein energy malnutrition in the first year of life. Front. Neurosci. 13:1222. 10.3389/fnins.2019.01222
    1. Cai J., Kim J. L., Baumeister T. R., Zhu M., Wang Y., Liu A., et al. (2022). A Multi-sequence MRI study in Parkinson’s disease: association between rigidity and myelin. J. Magn. Reson. Imaging 55 451–462. 10.1002/jmri.27853
    1. Calabrò R. S., Naro A., Filoni S., Pullia M., Billeri L., Tomasello P., et al. (2019). Walking to your right music: a randomized controlled trial on the novel use of treadmill plus music in Parkinson’s disease. J. Neuroeng. Rehabil. 16:68. 10.1186/s12984-019-0533-9
    1. Caviness J. N., Beach T. G., Hentz J. G., Shill H. A., Driver-Dunckley E. D., Adler C. H. (2018). Association between pathology and electroencephalographic activity in Parkinson’s disease. Clin. EEG Neurosci. 49 321–327. 10.1177/1550059417696179
    1. Caviness J. N., Hentz J. G., Evidente V. G., Driver-Dunckley E., Samanta J., Mahant P., et al. (2007). Both early and late cognitive dysfunction affects the electroencephalogram in Parkinson’s disease. Parkinsonism Relat. Disord. 13 348–354. 10.1016/j.parkreldis.2007.01.003
    1. Cozac V. V., Gschwandtner U., Hatz F., Hardmeier M., Rüegg S., Fuhr P. (2016). Quantitative EEG and cognitive decline in Parkinson’s disease. Parkinsons. Dis. 2016:9060649. 10.1155/2016/9060649
    1. Delis D. C., Kaplan E., Kramer J. H. (2001). Delis-Kaplan Executive Function System:Technical Manual. San Antonio, TX: Harcourt Assessment Company.
    1. Delorme A., Makeig S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134 9–21. 10.1016/j.jneumeth.2003.10.009
    1. Du L., Huang H., Yan J., Kim S., Risacher S. L., Inlow M., et al. (2016). Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method. Bioinformatics 32 1544–1551. 10.1093/bioinformatics/btw033
    1. Dubois B., Slachevsky A., Litvan I., Pillon B. (2000). The FAB: a frontal assessment battery at bedside. Neurology 55 1621–1626.
    1. Evans A., Collins D., Mills S. R., Brown E., Kelly R., Peters T. (1993). “3D statistical neuroanatomical models from 305 MRI volumes,” in Proceedings of the 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, (San Francisco, CA: IEEE; ), 1813–1817. 10.1109/NSSMIC.1993.373602
    1. Feigin V. L., Abajobir A. A., Abate K. H., Abd-Allah F., Abdulle A. M., Abera S. F. (2017). GBD 2015 neurological disorders collaborator group. global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the global burden of disease study 2015. Lancet Neurol. 16 877–897. 10.1016/S1474-4422(17)30299-5
    1. Folstein M., Robins N., Helzer J. (1983). The mini-mental state examination. Arch. Gen. Psychiatry 40:812.
    1. Garcia Llano M., Pedroso Ibanez I., Morales Chacon L., Rodriguez Obaya T., Perez Ruiz L., Sosa Teste I., et al. (2021). Short-term tolerance of nasally-administered NeuroEPO in patients with Parkinson disease. MEDICC Rev. 23, 49–54.
    1. Garzón F., Rodríguez Y., García-Rodríguez J. C., Rama R. (2018b). Neuroprotective effects of neuroEPO using an in vitro model of stroke. Behav. Sci. (Basel) 8:26. 10.3390/bs8020026
    1. Garzón F., Coimbra D., Parcerisas A., Rodriguez Y., Garcia J. C., Soriano E., et al. (2018a). NeuroEPO preserves neurons from glutamate-induced excitotoxicity. J. Alzheimers Dis. 65 1469–1483. 10.3233/JAD-180668
    1. Goetz C. G., Fahn S., Martinez-Martin P., Poewe W., Sampaio C., Stebbins G. T., et al. (2007). Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): process, format, and clinimetric testing plan. Mov. Disord. 22 41–47. 10.1002/mds.21198
    1. Goldsmith K. A., MacKinnon D. P., Chalder T., White P. D., Sharpe M., Pickles A. (2018). Tutorial: the practical application of longitudinal structural equation mediation models in clinical trials. Psychol. Methods 23 191–207. 10.1037/met0000154
    1. Green P., MacLeod C. J. (2016). SIMR?: an R package for power analysis of generalized linear mixed models by simulation. Methods Ecol. Evol. 7 493–498. 10.1111/2041-210X.12504
    1. Gu Y., Chen Y., Lu Y., Pan S. (2014). Integrative frequency power of EEG correlates with progression of mild cognitive impairment to dementia in Parkinson’s disease. Clin. EEG Neurosci. Neurosci. 47 113–117. 10.1177/1550059414543796
    1. Hernandez J. L., Valdes P., Biscay R., Virues T., Szava S., Bosch J., et al. (1994). A global scale factor in brain topography. Int J Neurosci 76 267–278. 10.3109/00207459408986009
    1. Herrero M., Blanch J., Peri J., De Pablo J., Pintor L., Bulbena A. (2003). A validation study of the hospital anxiety and depression scale (HADS) in a Spanish population. Gen. Hosp. Psychiatry 25 277–283. 10.1016/S0163-8343(03)00043-4
    1. Hoehn M. M., Yahr M. D. (1967). Parkinsonism: onset, progression and mortality. Neurology 17, 427–442.
    1. Horton R. (2006). Phase I clinical trials: a call for papers. Lancet 368 827–828. 10.1016/S0140-6736(06)69305-7
    1. Imai K., Keele L., Tingley D. (2010). A general approach to causal mediation analysis. Psychol. Methods 15 309–334. 10.1037/a0020761
    1. Jang W., Park J., Shin K. J., Kim J.-S., Kim J. S., Youn J., et al. (2014). Safety and efficacy of recombinant human erythropoietin treatment of non-motor symptoms in Parkinson’s disease. J. Neurol. Sci. 337 47–54. 10.1016/j.jns.2013.11.015
    1. Jendoubi T., Strimmer K. (2019). A whitening approach to probabilistic canonical correlation analysis for omics data integration. BMC Bioinformatics 20:15. 10.1186/s12859-018-2572-9
    1. Kadota T., Shingo T., Yasuhara T., Tajiri N., Kondo A., Morimoto T., et al. (2009). Continuous intraventricular infusion of erythropoietin exerts neuroprotective/rescue effects upon Parkinson’s disease model of rats with enhanced neurogenesis. Brain Res. 1254 120–127. 10.1016/j.brainres.2008.11.094
    1. Kanaan N. M., Collier T. J., Marchionini D. M., Mcguire S. O., Fleming M. F., Sortwell C. E. (2005). Exogenous erythropoietin provides neuroprotection of grafted dopamine neurons in a rodent model of Parkinson’ s disease. Brain Res. 1068 221–229. 10.1016/j.brainres.2005.10.078
    1. Kanaan N. M., Collier T. J., Marchionini D. M., McGuire S. O., Fleming M. F., Sortwell C. E. (2006). Exogenous erythropoietin provides neuroprotection of grafted dopamine neurons in a rodent model of Parkinson’s disease. Brain Res. 1068 221–229.
    1. Kessy A., Lewin A., Strimmer K. (2018). Optimal whitening and decorrelation. Am. Stat. 72 309–314. 10.1080/00031305.2016.1277159
    1. Klassen B. T., Hentz J. G., Shill H. A., Driver-Dunckley E., Evidente V. G. H., Sabbagh M. N., et al. (2011). Quantitative EEG as a predictive biomarker for Parkinson disease dementia. Neurology 77 118–124. 10.1212/WNL.0b013e318224af8d
    1. Liu Z., Whitaker K. J., Smith S. M., Nichols T. E. (2021). Improved Interpretability of brain-behavior CCA with domain-driven dimension reduction. bioRxiv [Preprint] 1–52. 10.1101/2021.07.12.451975v1
    1. Mattis S. (1988). Dementia Rating Scale (DRS). Odessa, FL: Psychological Assessment Resources, 1–2.
    1. McPherson R. J., Juul S. E. (2008). Recent trends in erythropoietin-mediated neuroprotection. Int. J. Dev. Neurosci. 26 103–111. 10.1016/j.ijdevneu.2007.08.012
    1. Merelli A., Czornyj L., Lazarowski A. (2015). Erythropoietin as a new therapeutic opportunity in brain inflammation and neurodegenerative diseases. Int. J. Neurosci. 125 793–797. 10.3109/00207454.2014.989321
    1. Møller J. (1986). Bartlett adjustments for structured covariances. Scand. J. Stat. 13 1–15.
    1. Pedroso I., Bringas M. L., Aguiar A., Morales L., Álvarez M., Valdés P. A., et al. (2012). Use of cuban recombinant human erythropoietin in Parkinson’s disease treatment. MEDICC Rev. 14 11–17. 10.37757/MR2012V14.N1.4
    1. Pedroso I., Garcia M., Casabona E., Morales L., Bringas M. L., Pérez L., et al. (2018). Protective activity of erythropoyetine in the cognition of patients with Parkinson’s disease. Behav. Sci. 8, 1–9. 10.3390/bs8050051
    1. Rahman S., Griffin H. J., Quinn N. P., Jahanshahi M. (2008). Quality of life in Parkinson’s disease: the relative importance of the symptoms. Mov. Disord. 23 1428–1434. 10.1002/mds.21667
    1. Rey A. (1941). L’examen psychologique dans les cas d’encéphalopathie traumatique. The psychological examination in cases of traumatic encepholopathy. Problems. Arch. Psychol. 28 215–285.
    1. Rey F., Balsari A., Giallongo T., Ottolenghi S., Di Giulio A. M., Samaja M., et al. (2019). Erythropoietin as a neuroprotective molecule: an overview of its therapeutic potential in neurodegenerative diseases. ASN Neuro 11 175909141987142. 10.1177/1759091419871420
    1. Reynolds C. R. (2002). Comprehensive Trail Making Test (CTMT). Austin, TX: Pro-Ed.
    1. Rivera D., Perrin P. B., Stevens L. F., Garza M. T., Weil C., Saracho C. P., et al. (2015). Stroop color-word interference test: normative data for the Latin American Spanish speaking adult population. NeuroRehabilitation 37 591–624. 10.3233/NRE-151281
    1. Rodríguez Cruz Y., Strehaiano M., Rodríguez Obaya T., García Rodríguez J. C., Maurice T. (2016). An Intranasal formulation of erythropoietin (Neuro-EPO) prevents memory deficits and amyloid toxicity in the APPSwe transgenic mouse model of Alzheimer’s disease. J. Alzheimers Dis. 55 231–248. 10.3233/JAD-160500
    1. Rodríguez Cruz Y., Yuneidys Mengana T., Muñoz Cernuda A., Subirós Martines N., González-Quevedo A., Sosa Testé I., et al. (2010). Treatment with Nasal Neuro-EPO improves the neurological, cognitive, and histological state in a gerbil model of focal ischemia. Sci. World J. 10 2288–2300. 10.1100/tsw.2010.215
    1. Santos-Morales O., Díaz-Machado A., Jiménez-Rodríguez D., Pomares-Iturralde Y., Festary-Casanovas T., González-Delgado C. A., et al. (2017). Nasal administration of the neuroprotective candidate NeuroEPO to healthy volunteers: a randomized, parallel, open-label safety study. BMC Neurol. 17:129. 10.1186/s12883-017-0908-0
    1. Sargin D., Friedrichs H., El-Kordi A., Ehrenreich H. (2010). Erythropoietin as neuroprotective and neuroregenerative treatment strategy: comprehensive overview of 12 years of preclinical and clinical research. Best Pract. Res. Clin. Anaesthesiol. 24 573–594. 10.1016/j.bpa.2010.10.005
    1. Schade S., Mollenhauer B., Trenkwalder C. (2020). Levodopa equivalent dose conversion factors: an updated proposal including opicapone and safinamide. Mov. Disord. Clin. Pract. 7 343–345. 10.1002/mdc3.12921
    1. Schrag A., Hovris A., Morley D., Quinn N., Jahanshahi M. (2006). Caregiver-burden in Parkinson’s disease is closely associated with psychiatric symptoms, falls, and disability. Parkinsonism Relat. Disord. 12 35–41. 10.1016/j.parkreldis.2005.06.011
    1. Schrag A., Jahanshahi M., Quinn N. (2000). What contributes to quality of life in patients with Parkinson’s disease? J. Neurol. Neurosurg. Psychiatry 69 308–312.
    1. Seer C., Lange F., Georgiev D., Jahanshahi M., Kopp B. (2016). Event-related potentials and cognition in Parkinson’s disease: an integrative review. Neurosci. Biobehav. Rev. 71 691–714. 10.1016/j.neubiorev.2016.08.003
    1. Shirahige L., Berenguer-Rocha M., Mendonça S., Rocha S., Rodrigues M. C., Monte-Silva K. (2020). Quantitative electroencephalography characteristics for Parkinson’s disease: a systematic review. J. Parkinsons. Dis. 10 455–470. 10.3233/JPD-191840
    1. Smith S. M., Nichols T. E. (2018). Statistical challenges in “Big Data’ human neuroimaging. Neuron 97 263–268. 10.1016/j.neuron.2017.12.018
    1. Strimmer K., Jendoubi T., Kessy A., Lewin A. (2021). whitening: Whitening and High-Dimensional Canonical Correlation Analysis.
    1. Taboada-Crispi A., Bringas-vega M. L., Bosch-bayard J., Isenhart R., Calzada-reyes A., Virues-alba T., et al. (2018). Quantitative EEG tomography of early childhood malnutrition. Front. Neurosci. 12:595. 10.3389/fnins.2018.00595
    1. Tingley D., Yamamoto T., Hirose K., Keele L., Imai K. (2019). Package “mediation”. Causal Mediation Analysis. 10.1214/10
    1. VanderWeele T. (2015). Explanation in Causal Inference. Oxford: Oxford University Press.
    1. Waninger S., Berka C., Stevanovic Karic M., Korszen S., Mozley P. D., Henchcliffe C., et al. (2020). Neurophysiological biomarkers of Parkinson’s disease. J. Parkinsons. Dis. 10 471–480. 10.3233/JPD-191844
    1. Wechsler D. (1997). WAIS-III. San Antonio, TX: Psychological Corporation.
    1. Xue Y. Q., Ma B. F., Zhao L. R., Tatom J. B., Li B., Jiang L. X., et al. (2010). AAV9-mediated erythropoietin gene delivery into the brain protects nigral dopaminergic neurons in a rat model of Parkinson’s disease. Gene Ther. 17 83–94. 10.1038/gt.2009.113
    1. Xue Y., Ma B., Zhao L., Tatom J. B., Li B., Jiang L., et al. (2009). AAV9-mediated erythropoietin gene delivery into the brain protects nigral dopaminergic neurons in a rat model of Parkinson’ s disease. Gene Ther. 17 83–94.
    1. Xue Y., Zhao L., Guo W. (2007). Intrastriatal administration of erythropoietin protects dopaminergic neurons and improves neurobehavioral outcome in a rat model of Parkinson”s disease. Neuroscience 146 1245–1258. 10.1016/j.neuroscience.2007.02.004
    1. Zhang J. (1998). Data sphering: some properties and applications. Ann. Inst. Stat. Math. 50 223–240. 10.1023/A:1003482914021
    1. Zhang W., Paul S. E., Winkler A., Bogdan R., Bijsterbosch J. D. (2022). Shared brain and genetic architectures between mental health and physical activity. medArxiv [Preprint] 1–29. 10.1101/2021.12.28.21268486
    1. Zimmermann R., Gschwandtner U., Hatz F., Schindler C., Bousleiman H., Ahmed S., et al. (2015). Correlation of EEG slowing with cognitive domains in nondemented patients with Parkinson’s disease. Dement. Geriatr. Cogn. Disord. 39 207–214. 10.1159/000370110

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe