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SYSTEMATIC REVIEW PROTOCOLS

Effects of transcranial direct current stimulation on heart rate variability: a systematic review protocol

Dias, Ingrid Alves1; Hazime, Fuad Ahmad1; Lopes, Danilo Alves1; Silva, Cristiano Sales da1; Baptista, Abrahão Fontes2; Silva, Baldomero Antonio Kato da1

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doi: 10.11124/JBISRIR-D-19-00134
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Abstract

Introduction

Heart rate (HR) autonomic modulation can be expressed by heart rate variability (HRV) analysis. Some evidence shows that the dysregulation of the cardiac autonomic system is associated with different health conditions such as obesity,1 depression,2 and heart failure.3 Moreover, dysregulation of HR autonomic modulation has been associated with a high risk of developing coronary disease4 and a higher rate of mortality after myocardial infarction.5 Balance of autonomic regulation has been investigated by analyzing HRV, since it is a simple, safe, noninvasive, and low-cost method that can quantify modulation of the autonomic nervous system (ANS)6,7 and evaluate cardiac risk in a variety of clinical conditions.7-9

Alterations in beat-to-beat behavior, indexed by HRV, can serve as indicators of the capacity to regulate intrinsic or extrinsic threats,10 and are an important outcome proxy to diagnose heart health impairments.11 Heart rate variability studies are conducted using signal processing methods and time series analysis in the frequency and time domain. Time domain analysis is related to global autonomic modulation, and is conducted to evaluate differences between RR normal intervals (NN) (defined as the temporal distance between consecutive normal beats [sinoatrial depolarizations] between the R peaks of the QRS complex). Time domain analysis enables measurement of these differences, including standard deviation of NN intervals (SDNN), standard deviation of mean RR intervals (SDANN), root mean square differences of successive RR intervals (RMSSD), and the percentage of normal RR intervals that differ by 50 ms (pNN50). Frequency domain analysis is conducted to evaluate two components: high frequency (HF) (0.14 and 0.40 Hz), related to the parasympathetic nervous system, and low frequency (LF) (0.004 and 0.15 Hz), related to the sympathetic and parasympathetic nervous system, with predominance of the sympathetic component.9,12

There is some evidence that altered HRV parameters are associated with different clinical disorders; for example, diminished low- and high-frequency domain in acute myocardial infarction syndrome,13 diminished parasympathetic activity in chronic pain (time domain—RMSSD),14 and diminished SDNN index in ischemic cardiomyopathy.15 For health conditions, HRV indexes are higher in subjects who have higher functional capacity, suggesting that physical conditioning enhances the heart's adaptive capacity.16 Dysregulation of heart autonomic modulation, induced by different health conditions, is strongly linked to the central nervous system (CNS). This brain–heart interaction is regulated by the ANS via parasympathetic and sympathetic nervous modulation.17,18 Previous studies19,20 have proposed a neurovisceral integration stress model. Neuroimaging studies17,18,21 combining HRV analysis have shown a significant association between cardiovagal activity and brain areas such as the parabrachial nucleus/locus ceruleus, cerebellum, periaqueductal gray, hypothalamus, amygdale, and insular and dorsomedial prefrontal cortices.

Balance of the brain–heart activation system is very important since imbalances may lead to an alteration in HRV, which directly affects the adaptive capacity of cardiovascular modulation.14 Since cardiovagal activity responds directly to brain activity, the brain could be a potential target to regulate ANS dysfunction-related diseases. Transcranial direct current stimulation (tDCS) is a noninvasive strategy to modulate brain excitability, which has achieved promising results in different health conditions. However, in the setting of HRV, tDCS has demonstrated mixed results.22-25

The clinical applicability of tDCS in patients with cardiovascular dysautonomia is still unclear, as studies on this topic have demonstrated differences in tDCS parametrization and cortical target areas as well as conflicting results on sympathetic and parasympathetic activity. A study with 14 subjects who underwent tDCS for 15 minutes showed significant changes in HRV, with a predominance of the sympathetic component (increase in LF power and LF/HF ratio).25 Other studies evaluating subjects in good health,26-28 with depression,24 and with spinal cord lesions23 demonstrated that tDCS had a positive effect on HRV, with a predominance of the parasympathetic component (increased SDNN and HF and decreased LF and LF/HF ratio). Taken together, these results suggest that tDCS may have a modulatory effect on HRV, which means it may have clinical applicability in the setting of ANS dysfunction. On the other hand, a study with smokers found that tDCS did not significantly affect HRV during exposure to a stimulus.22

A systematic review of noninvasive brain stimulation techniques for the ANS found conflicting clinical benefits. This review highlighted the lack of consensus regarding the best stimulation parameters to induce modifications in heart autonomic function, as well as the shortage of studies with satisfactory methodological quality to recommend the safe application of noninvasive brain stimulation techniques.29 Another meta-analysis, which investigated the association between neural stimulation techniques and cardiovascular system functioning, showed promising evidence that these techniques may serve as therapeutic tools to modulate HRV. In these studies the size of the effect was small but significant, and may be comparable to the effect seen after lifestyle interventions such as smoking cessation or increased exercise.30

Other than these two systematic reviews,29,30 there has been no attempt to systematically evaluate and quantify the isolated effects of tDCS on the measurements in the frequency domain and/or in the time domain of HRV in healthy subjects and/or in those with disorders. In this review, we aim to clarify current evidence about the effect of tDCS on HR autonomic modulation and highlight the relationship between tDCS parametrization and the facilitation/inhibition effect on the autonomic system (sympathetic or parasympathetic tone). Thus, the results of this review will be of fundamental interest to researchers, health professionals, and patients and may lead to more evidence regarding the safe use of tDCS as an alternative to or in combination with drugs for the treatment and prevention of clinical disorders.

Review question/objective

What are the effects of tDCS on heart rate modulation (indexed by HRV parameters) in healthy individuals and those with clinical disorders?

Inclusion criteria

Participants

This review will consider studies that include healthy participants or those with clinical disorders (including but not limited to chronic pain condition, psychiatric disorders, stroke, or cardiovascular autonomic dysfunction). This review will exclude studies with participants under the age of 18.

Intervention

This review will consider studies that evaluate tDCS (irrespective of the current intensity and density, time of stimulation, electrode montage and single or multiple doses, and target brain areas).

Comparator

This review will consider studies that compare the intervention to sham tDCS or to a control group. This may include standard treatments for a specific clinical disorder, medications, or other neuromodulation techniques such as deep brain stimulation, transcranial magnetic stimulation, or vagus nerve stimulation. Multi-component intervention (e.g. medication plus tDCS compared to medication alone) will also be included.

Outcomes

This review will consider studies that include parasympathetic and sympathetic activity as primary and secondary outcomes, respectively. Time and frequency domains (including but not limited to SDNN, SDANN, RMSSD, pNN50 and HF, LF, LF/HF, respectively) will be considered as HRV outcomes, measured before, during, or after tDCS stimulation.

Types of studies

This review will consider randomized and non-randomized clinical trials, and experimental, quasi-experimental designs, theses, and dissertations that use HRV measurements. Case reports and systematic reviews will be excluded.

Studies published in any language will be included, and no date restrictions will be applied.

Methods

The proposed systematic review will be conducted in accordance with the JBI methodology for systematic reviews of effectiveness evidence31 and PRISMA recommendations (Preferred Reporting Items for Systematic reviews and Meta-Analyses)32 and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42018114105).

Search strategy

The search strategy will aim to identify both published and unpublished studies. An initial limited search of PubMed was undertaken to identify articles on the topic. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles, were used to develop a full search strategy for the PubMed database (see Appendix I). The search strategy will be adapted for each included information source. The reference lists of all studies included in the review will also be searched for additional studies.

Information sources

The databases to be searched include: PubMed, Embase, CINAHL, Web of Science, PsycNET, Cochrane Controlled Register of Trials (CENTRAL) and Physiotherapy Evidence Database (PEDro). The search for unpublished studies and gray literature will include ProQuest Disserts and Theses Database Science, Web of Science, BIOSIS Previews, Grey Literature Report and the Proceedings database.

Study selection

Following the search, all identified citations will be collated and uploaded into EndNote version X8 (Clarivate Analytics, PA, USA) and duplicates will be removed. Two independent reviewers will then screen study titles and abstracts according to the inclusion criteria for the review. Potentially relevant studies will be retrieved in full, and their citation details imported into the JBI System for the Unified Management, Assessment, and Review of Information (JBI SUMARI; JBI, Adelaide, Australia). Two independent reviewers will assess the full text of selected articles and documents in detail against the inclusion and exclusion criteria. Reasons for exclusion of full-text studies that do not meet the inclusion criteria will be recorded and reported in the systematic review. Any disagreements that arise between the reviewers at each stage of the study selection process will be resolved through discussion, or with a third reviewer. The results of the search will be reported in full in the final systematic review and presented in a PRISMA flow diagram.32

Assessment of methodological quality

Eligible studies will be critically appraised, scored, and qualified by two independent reviewers using the standardized critical appraisal instruments from the JBI.31 Authors of studies will be contacted to request missing or additional data for clarification, where required.

Any disagreements that arise between the reviewers will be arbitrated by a third investigator. The results of critical appraisal will be reported in narrative and/or tabular formats. All included studies, regardless of the results of their methodological quality, will undergo data extraction and synthesis (where possible).

Data extraction

Two independent reviewers will extract data from studies included in the review using the standardized JBI data extraction tool. The data extracted will include authors, year of publication, study design, health conditions, sample size, sample characteristics, tDCS parameters (stimulation montage and polarity), duration of session (min), current intensity (mA), size of electrodes (cm2), current density (mA/cm2), comparator (sham tDCS or control), HRV outcomes (time and frequency domain measures) and adverse effects. Any disagreements that arise between the reviewers will be arbitrated by a third investigator. Authors of studies will be contacted to request missing or additional data, where required.

Data synthesis

Quantitative data will, where possible, be pooled with statistical meta-analysis using JBI SUMARI. Effect sizes will be expressed as weighted (or standardized) final post-intervention mean differences (for continuous data) and their 95% confidence intervals will be calculated for analysis. Heterogeneity will be assessed statistically using the standard chi-squared and I2 tests. The choice of model (random or fixed effects) and method for meta-analysis will be based on the guidance by Tufanaru et al.33 Subgroup analyses (effects for healthy subjects compared to subjects with any clinical disorders) will be conducted where there are sufficient data to investigate. Sensitivity analyses will be conducted to test decisions made regarding the effectiveness of interventions. Where statistical pooling is not possible, the findings will be presented in narrative form, including tables and figures to aid in data presentation where appropriate.

A funnel plot will be generated to assess publication bias if there are 10 or more studies included in a meta-analysis. Statistical tests for funnel plot asymmetry will be performed where appropriate.

Assessing certainty in the findings

The Grading of Recommendations, Assessment, Development and Evaluation (GRADE)34 approach for grading the quality of evidence will be followed and a Summary of Findings (SoF) will be created using GRADEPro GDT software (McMaster University, ON, Canada). The SoF will present the following information where appropriate: absolute risks for treatment and control, estimates of relative risk, and a ranking of the quality of the evidence based on study limitations (risk of bias), indirectness, inconsistency, imprecision and publication bias.35 The following outcomes will be included in the SoF: parasympathetic and sympathetic activity indexed by heart rate variability parameters.

Acknowledgments

This review will contribute to a Masters in Biomedical Sciences at Federal University of Piauí for IAD.

Appendix I: Search strategy

PubMed. Search conducted during September 2019.

References

1. Amano M, Kanda T, Ue H, Moritani T. Exercise training and autonomic nervous system activity in obese individuals. Med Sci Sports Exerc 2001; 33 (8):1287–1291.
2. Agelink MW, Majewski T, Wurthmann C, Postert T, Linka T, Rotterdam S, et al. Autonomic neurocardiac function in patients with major depression and effects of antidepressive treatment with nefazodone. J Affect Disord 2001; 62 (3):187–198.
3. Garet M, Degache F, Pichot V, Duverney D, Costes F, Da Costa A, et al. Relationship between daily physical activity and ANS activity in patients with CHF. Med Sci Sports Exerc 2005; 7 (8):1257–1263.
4. Liao D, Carnethon M, Evans GW, Cascio WE, Heiss G. Lower heart rate variability is associated with the development of coronary heart disease in individuals with diabetes: the atherosclerosis risk in communities (ARIC) study. Diabetes 2002; 51 (12):3524–3531.
5. Camm AJ, Pratt CM, Schwartz PJ, Al-Khalidi HR, Spyt MJ, Holroyde MJ, et al. Mortality in patients after a recent myocardial infarction: a randomized, placebo-controlled trial of azimilide using heart rate variability for risk stratification. Circulation 2004; 109 (8):990–996.
6. Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996; 93 (5):1043–1065.
7. Vanderlei LC, Pastre CM, Hoshi RA, Carvalho TD, Godoy MF. Basic notions of heart rate variability and its clinical applicability. Rev Bras Cir Cardiovasc 2009; 24 (2):205–217.
8. Bernardi L, Spallone V, Stevens M, Hilsted J, Frontoni S, Pop-Busui R, et al. Methods of investigation for cardiac autonomic dysfunction in human research studies. Diabetes Metab Res Rev 2011; 27 (7):654–664.
9. Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, et al. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace 2015; 17 (9):1341–1353.
10. De Jonckheere J, Rommel D, Nandrino JL, Jeanne M, Logier R. Heart rate variability analysis as an index of emotion regulation processes: interest of the Analgesia Nociception Index (ANI). Conf Proc IEEE Eng Med Biol Soc 2012; 2012:3432–3435.
11. Gang Y, Malik M. Heart rate variability analysis in general medicine. Indian Pacing Electrophysiol J 2003; 3 (1):34–40.
12. Pumprla J, Howorka K, Groves D, Chester M, Nolan J. Functional assessment of heart rate variability: physiological basis and practical applications. Int J Cardiol 2002; 84 (1):1–14.
13. Chen CF, Lin HF, Lin RT, Yang YH, Lai CL. Relationship between ischemic stroke location and autonomic cardiac function. J Clin Neurosci 2013; 20 (3):406–409.
14. Koenig J, Loerbroks A, Jarczok MN, Fischer JE, Thayer JF. Chronic pain and heart rate variability in a cross-sectional occupational sample: evidence for impaired vagal control. Clin J Pain 2016; 32 (3):218–225.
15. Bilchick KC, Fetics B, Djoukeng R, Fisher SG, Fletcher RD, Singh SN, et al. Prognostic value of heart rate variability in chronic congestive heart failure (Veterans Affairs’ Survival Trial of Antiarrhythmic Therapy in Congestive Heart Failure). Am J Cardiol 2002; 90 (1):24–28.
16. Antelmi I, de Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol 2004; 93 (3):381–385.
17. Lane RD, McRae K, Reiman EM, Chen K, Ahern GL, Thayer JF. Neural correlates of heart rate variability during emotion. Neuroimage 2009; 44 (1):213–222.
18. Napadow V, Dhond R, Conti G, Makris N, Brown EN, Barbieri R. Brain correlates of autonomic modulation: combining heart rate variability with fMRI. Neuroimage 2008; 42 (1):169–177.
19. Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev 2009; 33 (2):81–88.
20. Thayer JF, Lane RD. A model of neurovisceral integration in emotion regulation and dysregulation. J Affect Disord 2000; 61 (3):201–216.
21. Gianaros PJ, Van Der Veen FM, Jennings JR. Regional cerebral blood flow correlates with heart period and high-frequency heart period variability during working-memory tasks: implications for the cortical and subcortical regulation of cardiac autonomic activity. Psychophysiology 2004; 41 (4):521–530.
22. Kroczek AM, Haussinger FB, Rohe T, Schneider S, Plewnia C, Batra A, et al. Effects of transcranial direct current stimulation on craving, heart-rate variability and prefrontal hemodynamics during smoking cue exposure. Drug Alcohol Depend 2016; 168:123–127.
23. da Silva FT, Browne RA, Pinto CB, Saleh Velez FG, do Egito ES, do Rego JT, et al. Transcranial direct current stimulation in individuals with spinal cord injury: Assessment of autonomic nervous system activity. Restor Neurol Neurosci 2017; 35 (2):159–169.
24. Brunoni AR, Kemp AH, Dantas EM, Goulart AC, Nunes MA, Boggio PS, et al. Heart rate variability is a trait marker of major depressive disorder: evidence from the sertraline vs. electric current therapy to treat depression clinical study. Int J Neuropsychopharmacol 2013; 16 (9):1937–1949.
25. Clancy JA, Johnson R, Raw R, Deuchars SA, Deuchars J. Anodal transcranial direct current stimulation (tDCS) over the motor cortex increases sympathetic nerve activity. Brain Stimul 2014; 7 (1):97–104.
26. Petrocchi N, Piccirillo G, Fiorucci C, Moscucci F, Di Iorio C, Mastropietri F, et al. Transcranial direct current stimulation enhances soothing positive affect and vagal tone. Neuropsychologia 2017; 96:256–261.
27. Montenegro RA, Farinatti Pde T, Fontes EB, Soares PP, Cunha FA, Gurgel JL, et al. Transcranial direct current stimulation influences the cardiac autonomic nervous control. Neurosci Lett 2011; 497 (1):32–36.
28. Nikolin S, Boonstra TW, Loo CK, Martin D. Combined effect of prefrontal transcranial direct current stimulation and a working memory task on heart rate variability. PLoS One 2017; 12 (8):e0181833.
29. Schestatsky P, Simis M, Freeman R, Pascual-Leone A, Fregni F. Non-invasive brain stimulation and the autonomic nervous system. Clin Neurophysiol 2013; 124 (9):1716–1728.
30. Makovac E, Thayer JF, Ottaviani C. A meta-analysis of non-invasive brain stimulation and autonomic functioning: Implications for brain-heart pathways to cardiovascular disease. Neurosci Biobehav Rev 2017; 74 (Pt B):330–341.
31. Tufanaru C, Munn Z, Aromataris E, Campbell J, Hopp L. Chapter 3: Systematic reviews of effectiveness. In: Aromataris E, Munn Z (Editors). JBI Reviewer's Manual: Adelaide: JBI [Internet]. 2017 [cited 2019 Oct 29]. Available from: https://reviewersmanual.joannabriggs.org/.
32. Moher D, Liberati A, Tetzlaff J, Altman DG. the PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6 (7):e1000097.
33. Tufanaru C, Munn Z, Stephenson M, Aromataris E. Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness. Int J Evid Based Healthc 2015; 13 (3):196–207.
34. Schünemann H, Brożek J, Guyatt G, Oxman A, editors. Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach. Updated October 2013. The GRADE Working Group, 2013. Available from: https://gdt.gradepro.org/app/handbook/handbook.html.
35. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction — GRADE evidence profiles and summary of findings tables. J Clin Epidemiol 2011; 64 (4):383–394.
Keywords:

Autonomic nervous system; heart rate; parasympathetic nervous system; sympathetic nervous system; transcranial direct current stimulation (tDCS)

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