# High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. The Data

#### 2.1. Participants

#### 2.2. Intervention

^{®}, New York, NY, USA). During and after the application of the electric current, we assessed the body movement kinetics measured by two force plates (Bertec 4060-NC, Columbus, OH, USA) in the static orthostatic posture of each individual, verified by the WBA (Figure 1).

#### 2.3. Outcome Measure

#### 2.4. Statistical Analyses

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Estimate | Std. Error | t Value | Pr(>|z|) | ||
---|---|---|---|---|---|

baseline | −2.8219 | 0.1203 | −23.46 | <2 × 10${}^{-16}$ | *** |

Anodal (AC) | −3.1613 | 0.1068 | −29.59 | <2 × 10${}^{-16}$ | *** |

Cathodal (CC) | −3.1495 | 0.1068 | −29.48 | <2 × 10${}^{-16}$ | *** |

SHAM-AC | −3.1958 | 0.1107 | −28.86 | <2 × 10${}^{-16}$ | *** |

SHAM-CC | −3.0344 | 0.1094 | −27.74 | <2 × 10${}^{-16}$ | *** |

1 mA vs. 0 mA | −0.1088 | 0.0649 | −1.68 | 0.0934 | . |

2 mA vs. 0 mA | 0.1073 | 0.0545 | 1.97 | 0.0490 | * |

3 mA vs. 0 mA | −0.0899 | 0.0597 | −1.51 | 0.1321 | |

Time | −0.0034 | 0.0006 | −6.13 | 8.6 × ${10}^{-10}$ | *** |

Right-side vs. Left- | 0.1457 | 0.041 | 3.55 | 0.0004 | *** |

AC vs. SHAM-CC: 1 mA | 0.1142 | 0.0809 | 1.41 | 0.1579 | |

CC vs. SHAM-CC: 1 mA | 0.1813 | 0.0804 | 2.25 | 0.0242 | * |

SHAM-AC vs. SHAM-CC: 1 mA | 0.02619 | 0.0968 | 0.27 | 0.7867 | |

AC vs. SHAM-CC: 2 mA | −0.0299 | 0.0678 | −0.44 | 0.6592 | |

CC vs. SHAM-CC: 2 mA | −0.1483 | 0.0679 | −2.18 | 0.0289 | * |

SHAM-AC vs. SHAM-CC: 2 mA | −0.158 | 0.0811 | −1.95 | 0.0515 | . |

AC vs. SHAM-CC: 3 mA | 0.0303 | 0.0743 | 0.41 | 0.6839 | |

CC vs. SHAM-CC: 3 mA | −0.0033 | 0.0744 | −0.04 | 0.9646 | |

SHAM-AC vs. SHAM-CC: 3 mA | 0.0584 | 0.0891 | 0.66 | 0.5124 | |

Right-side vs. Left-: Time | 0.0017 | 0.0008 | 2.17 | 0.0299 | * |

−−− | |||||

Signif. codes: | ‘***’ 0.001 | ‘**’ 0.01 | ‘*’ 0.05 | ‘.’ 0.1 | ‘ ’ 1 |

## Appendix B

Estimate | Std. Error | l-95% CI | u-95% CI | Rhat | Bulk ESS | Tail ESS | |
---|---|---|---|---|---|---|---|

baseline | −2.463 | 3.638 | −9.658 | 4.671 | 1.001 | 3438 | 3764 |

Anodal (AC) | −3.157 | 0.115 | −3.380 | −2.925 | 1.000 | 2128 | 3039 |

Cathodal (CC) | −3.146 | 0.114 | −3.368 | −2.912 | 1.001 | 2138 | 2968 |

SHAM-AC | −3.189 | 0.118 | −3.423 | −2.956 | 1.000 | 2204 | 2932 |

SHAM-CC | −3.028 | 0.116 | −3.253 | −2.794 | 1.001 | 2081 | 3043 |

1 mA vs. 0 mA | 0.076 | 4.543 | −8.888 | 9.022 | 1.000 | 3560 | 3726 |

2 mA vs. 0 mA | 0.435 | 4.318 | −8.009 | 8.579 | 1.000 | 3394 | 3553 |

3 mA vs. 0 mA | −0.274 | 4.337 | −8.635 | 8.395 | 1.000 | 3142 | 3523 |

Time | −0.003 | 0.001 | −0.005 | −0.002 | 1.000 | 4073 | 3580 |

Right-side vs. Left- | 0.146 | 0.041 | 0.064 | 0.227 | 1.000 | 3899 | 3913 |

AC vs. baseline: 1 mA | −0.071 | 4.543 | −9.004 | 8.854 | 1.000 | 3542 | 3663 |

CC vs. baseline: 1 mA | −0.004 | 4.542 | −8.956 | 9.027 | 1.000 | 3562 | 3682 |

SHAM-AC vs. baseline: 1 mA | −0.157 | 4.543 | −9.086 | 8.816 | 1.000 | 3555 | 3700 |

SHAM-CC vs. baseline: 1 mA | −0.185 | 4.540 | −9.130 | 8.746 | 1.000 | 3545 | 3668 |

AC vs. baseline: 2 mA | −0.358 | 4.318 | −8.493 | 8.080 | 1.000 | 3392 | 3520 |

CC vs. baseline: 2 mA | −0.476 | 4.317 | −8.660 | 7.978 | 1.000 | 3398 | 3587 |

SHAM-AC vs. baseline: 2 mA | −0.485 | 4.318 | −8.684 | 8.021 | 1.000 | 3392 | 3520 |

SHAM-CC vs. baseline: 2 mA | −0.328 | 4.318 | −8.480 | 8.128 | 1.000 | 3392 | 3553 |

AC vs. baseline: 3 mA | 0.214 | 4.337 | −8.458 | 8.524 | 1.000 | 3147 | 3537 |

CC vs. baseline: 3 mA | 0.181 | 4.338 | −8.502 | 8.534 | 1.000 | 3143 | 3524 |

SHAM-AC vs. baseline: 3 mA | 0.242 | 4.336 | −8.421 | 8.583 | 1.000 | 3146 | 3460 |

SHAM-CC vs. baseline: 3 mA | 0.183 | 4.337 | −8.485 | 8.534 | 1.000 | 3146 | 3538 |

Right-side vs. Left-: Time | 0.002 | 0.001 | 0.000 | 0.003 | 1.000 | 4098 | 3929 |

— | |||||||

shape parameter | 3.460 | 0.097 | 3.274 | 3.653 | 1.000 | 4261 | 4043 |

— | |||||||

sd(Intercept) | 0.473 | 0.084 | 0.346 | 0.673 | 1.000 | 3286 | 3929 |

## References

- Campbell, B.C.; De Silva, D.A.; Macleod, M.R.; Coutts, S.B.; Schwamm, L.H.; Davis, S.M.; Donnan, G.A. Ischaemic stroke. Nat. Rev. Dis. Prim.
**2019**, 5, 1–22. [Google Scholar] [CrossRef] [PubMed] - Capistrant, B.D.; Wang, Q.; Liu, S.Y.; Glymour, M.M. Stroke-associated differences in rates of activity of daily living loss emerge years before stroke onset. J. Am. Geriatr. Soc.
**2013**, 61, 931–938. [Google Scholar] [CrossRef] [PubMed] - Mizuta, N.; Hasui, N.; Nakatani, T.; Takamura, Y.; Fujii, S.; Tsutsumi, M.; Taguchi, J.; Morioka, S. Walking characteristics including mild motor paralysis and slow walking speed in post-stroke patients. Sci. Rep.
**2020**, 10, 1–10. [Google Scholar] [CrossRef] [PubMed] - Barra, J.; Oujamaa, L.; Chauvineau, V.; Rougier, P.; Pérennou, D. Asymmetric standing posture after stroke is related to a biased egocentric coordinate system. Neurology
**2009**, 72, 1582–1587. [Google Scholar] [CrossRef] - Bonan, I.; Leman, M.; Legargasson, J.; Guichard, J.; Yelnik, A. Evolution of subjective visual vertical perturbation after stroke. Neurorehabilit. Neural Repair
**2006**, 20, 484–491. [Google Scholar] [CrossRef] - Piscicelli, C.; Perennou, D. Visual verticality perception after stroke: A systematic review of methodological approaches and suggestions for standardization. Ann. Phys. Rehabil. Med.
**2017**, 60, 208–216. [Google Scholar] [CrossRef] - Pérennou, D. Postural disorders and spatial neglect in stroke patients: A strong association. Restor. Neurol. Neurosci.
**2006**, 24, 319–334. [Google Scholar] - Baggio, J.A.; Mazin, S.S.; Alessio-Alves, F.F.; Barros, C.G.; Carneiro, A.A.; Leite, J.P.; Pontes-Neto, O.M.; Santos-Pontelli, T.E. Verticality perceptions associate with postural control and functionality in stroke patients. PLoS ONE
**2016**, 11, e0150754. [Google Scholar] [CrossRef] [Green Version] - Kheradmand, A.; Winnick, A. Perception of upright: Multisensory convergence and the role of temporo-parietal cortex. Front. Neurol.
**2017**, 8, 552. [Google Scholar] [CrossRef] - Rossi, S.; Hallett, M.; Rossini, P.M.; Pascual-Leone, A.; Safety of TMS Consensus Group. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin. Neurophysiol.
**2009**, 120, 2008–2039. [Google Scholar] [CrossRef] [Green Version] - Santos-Pontelli, T.E.; Rimoli, B.P.; Favoretto, D.B.; Mazin, S.C.; Truong, D.Q.; Leite, J.P.; Pontes-Neto, O.M.; Babyar, S.R.; Reding, M.; Bikson, M.; et al. Polarity-dependent misperception of subjective visual vertical during and after transcranial direct current stimulation (tDCS). PLoS ONE
**2016**, 11, e0152331. [Google Scholar] [CrossRef] [PubMed] - Santos, T.E.; Favoretto, D.B.; Toostani, I.G.; Nascimento, D.C.; Rimoli, B.P.; Bergonzoni, E.; Lemos, T.W.; Truong, D.Q.; Delbem, A.C.; Makkiabadi, B.; et al. Manipulation of human verticality using high-definition transcranial direct current stimulation. Front. Neurol.
**2018**, 9, 825. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Baier, B.; Suchan, J.; Karnath, H.O.; Dieterich, M. Neural correlates of disturbed perception of verticality. Neurology
**2012**, 78, 728–735. [Google Scholar] [CrossRef] [PubMed] - Nascimento, D.C.; Depetri, G.; Stefano, L.H.; Anacleto, O.; Leite, J.P.; Edwards, D.J.; Santos, T.E.; Louzada Neto, F. Entropy analysis of high-definition transcranial electric stimulation effects on eeg dynamics. Brain Sci.
**2019**, 9, 208. [Google Scholar] [CrossRef] [Green Version] - Vicente, R.; Wibral, M.; Lindner, M.; Pipa, G. Transfer entropy—A model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci.
**2011**, 30, 45–67. [Google Scholar] [CrossRef] [Green Version] - Pincus, S.M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA
**1991**, 88, 2297–2301. [Google Scholar] [CrossRef] [Green Version] - Richman, J.S.; Moorman, J.R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circ. Physiol.
**2000**, 278, H2039–H2049. [Google Scholar] [CrossRef] [Green Version] - Fonteneau, C.; Mondino, M.; Arns, M.; Baeken, C.; Bikson, M.; Brunoni, A.R.; Burke, M.J.; Neuvonen, T.; Padberg, F.; Pascual-Leone, A.; et al. Sham tDCS: A hidden source of variability? Reflections for further blinded, controlled trials. Brain Stimul.
**2019**, 12, 668–673. [Google Scholar] [CrossRef] - Bossomaier, T.; Barnett, L.; Harré, M.; Lizier, J.T. Transfer entropy. In An Introduction to Transfer Entropy; Springer: Berlin/Heidelberg, Germany, 2016; pp. 65–95. [Google Scholar]
- Aziz, N.A. Transfer entropy as a tool for inferring causality from observational studies in epidemiology. bioRxiv
**2017**, 149625. [Google Scholar] - R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
- Mann, H.B.; Whitney, D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat.
**1947**, 18, 50–60. [Google Scholar] [CrossRef] - Wilcoxon, F. Individual Comparisons by Ranking Methods. Biom. Bull.
**1945**, 1, 80–83. [Google Scholar] [CrossRef] - Biabani, M.; Farrell, M.; Zoghi, M.; Egan, G.; Jaberzadeh, S. Crossover design in transcranial direct current stimulation studies on motor learning: Potential pitfalls and difficulties in interpretation of findings. Rev. Neurosci.
**2018**, 29, 463–473. [Google Scholar] [CrossRef] [PubMed] - Leshikar, E.D.; Leach, R.C.; McCurdy, M.P.; Trumbo, M.C.; Sklenar, A.M.; Frankenstein, A.N.; Matzen, L.E. Transcranial direct current stimulation of dorsolateral prefrontal cortex during encoding improves recall but not recognition memory. Neuropsychologia
**2017**, 106, 390–397. [Google Scholar] [CrossRef] [PubMed] - Long, J.A. Jtools: Analysis and Presentation of Social Scientific Data, 2020. R Package Version 2.1.0. Available online: https://cran.r-project.org/web/packages/jtools/index.html (accessed on 30 November 2021).
- Genthon, N.; Rougier, P. Influence of an asymmetrical body weight distribution on the control of undisturbed upright stance. J. Biomech.
**2005**, 38, 2037–2049. [Google Scholar] [CrossRef] [PubMed] - Anker, L.C.; Weerdesteyn, V.; van Nes, I.J.; Nienhuis, B.; Straatman, H.; Geurts, A.C. The relation between postural stability and weight distribution in healthy subjects. Gait Posture
**2008**, 27, 471–477. [Google Scholar] [CrossRef] [PubMed] - Kamphuis, J.F.; de Kam, D.; Geurts, A.C.; Weerdesteyn, V. Is weight-bearing asymmetry associated with postural instability after stroke? A systematic review. Stroke Res. Treat.
**2013**, 2013, 692137. [Google Scholar] [CrossRef] [PubMed] - Marigold, D.S.; Eng, J.J.; Tokuno, C.D.; Donnelly, C.A. Contribution of muscle strength and integration of afferent input to postural instability in persons with stroke. Neurorehabilt. Neural Repair
**2004**, 18, 222–229. [Google Scholar] [CrossRef] [Green Version] - Marigold, D.S.; Eng, J.J. The relationship of asymmetric weight-bearing with postural sway and visual reliance in stroke. Gait Posture
**2006**, 23, 249–255. [Google Scholar] [CrossRef] [Green Version] - Mansfield, A.; Danells, C.J.; Inness, E.; Mochizuki, G.; McIlroy, W.E. Between-limb synchronization for control of standing balance in individuals with stroke. Clin. Biomech.
**2011**, 26, 312–317. [Google Scholar] [CrossRef] [Green Version] - Skarda, C.A.; Freeman, W.J. How brains make chaos in order to make sense of the world. Behav. Brain Sci.
**1987**, 10, 161–173. [Google Scholar] [CrossRef] [Green Version] - Huang, Y.; Holzel, R.; Pethig, R.; Wang, X.B. Differences in the AC electrodynamics of viable and non-viable yeast cells determined through combined dielectrophoresis and electrorotation studies. Phys. Med. Biol.
**1992**, 37, 1499–1517. [Google Scholar] [CrossRef] [PubMed] - Chialvo, D.R.; Gilmour, R.F., Jr.; Jalife, J. Low dimensional chaos in cardiac tissue. Nature
**1990**, 343, 653–657. [Google Scholar] [CrossRef] [PubMed] - Stein, K.M.; Walden, J.; Lippman, N.; Lerman, B.B. Ventricular response in atrial fibrillation: Random or deterministic? Am. J. Physiol.-Heart Circ. Physiol.
**1999**, 277, H452–H458. [Google Scholar] [CrossRef] [PubMed] - Carroll, J.P.; Freedman, W. Nonstationary properties of postural sway. J. Biomech.
**1993**, 26, 409–416. [Google Scholar] [CrossRef] - Collins, J.J.; Luca, C.J.D. Open-loop and closed-loop control of posture: A random-walk analysis of center-of-pressure trajectories. Exp. Brain Res.
**1993**, 95, 308–318. [Google Scholar] [CrossRef] - Duarte, M.; Zatsiorsky, V.M. On the fractal properties of natural human standing. Neurosci. Lett.
**2000**, 283, 173–176. [Google Scholar] [CrossRef] - Gagey, P.M.; Martinerie, J.; Pezard, L.; Benaim, C. L’équilibre Statique Est Contrôlé par un Système Dynamique Non-Linéaire. 1998. Available online: http://ada-posturologie.fr/L'equilibrestatique1998.pdf (accessed on 28 February 2022).
- Kerk, J.; Snyder, A.C.; Schot, P.K.; Myklebust, B.M.; Prieto, T.; Myklebust, J.; O’Hagan, K.P.; Clifford, P.S. The Effect of an Abdominal Binder on the Exercise Response of Paraplegic Wheelchair Athletes: 188. Med. Sci. Sport. Exerc.
**1992**, 24, S32. [Google Scholar] [CrossRef] - Oie, K.S.; Kiemel, T.; Jeka, J.J. Human multisensory fusion of vision and touch: Detecting non-linearity with small changes in the sensory environment. Neurosci. Lett.
**2001**, 315, 113–116. [Google Scholar] [CrossRef] - Fino, P.C.; Mojdehi, A.R.; Adjerid, K.; Habibi, M.; Lockhart, T.E.; Ross, S.D. Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-fallers. Ann. Biomed. Eng.
**2015**, 44, 1636–1645. [Google Scholar] [CrossRef] - Donker, S.F.; Roerdink, M.; Greven, A.J.; Beek, P.J. Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control. Exp. Brain Res.
**2007**, 181, 1–11. [Google Scholar] [CrossRef] [Green Version] - Kędziorek, J.; Błażkiewicz, M. Nonlinear Measures to Evaluate Upright Postural Stability: A Systematic Review. Entropy
**2020**, 22, 1357. [Google Scholar] [CrossRef] [PubMed] - Lipsitz, L.A. Dynamics of stability: The physiologic basis of functional health and frailty. J. Gerontol. A Biol. Sci. Med. Sci.
**2002**, 57, B115–B125. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lipsitz, L.A.; Goldberger, A.L.; Goldberger, A.L. Loss of ‘complexity’ and aging. Potential applications of fractals and chaos theory to senescence. Jama
**1992**, 267, 1806–1809. [Google Scholar] [CrossRef] - Busa, M.A.; van Emmerik, R.E.A. Multiscale entropy: A tool for understanding the complexity of postural control. J. Sport Health Sci.
**2016**, 5, 44–51. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Louzada, F.; Nascimento, D.C.d.; Egbon, O.A. Spatial Statistical Models: An Overview under the Bayesian Approach. Axioms
**2021**, 10, 307. [Google Scholar] [CrossRef] - Paillard, T.; Noé, F. Techniques and methods for testing the postural function in healthy and pathological subjects. BioMed Res. Int.
**2015**, 2015, 891390. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bürkner, P.C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw.
**2017**, 80, 1–28. [Google Scholar] [CrossRef] [Green Version] - R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar]
- Team, S.D. Stan modeling language users guide and reference manual. Version 2.12. Tech. Rep.
**2016**. [Google Scholar]

**Figure 1.**Illustration of the WBA study protocol. The left-hand side figure exhibits the platforms. In the middle, an illustration of a participant positioned on this equipment. The right-hand side shows the position of the high definition transcranial stimulation (HD-tDCS) electrodes.

**Figure 2.**Visual representation of the data transformation method in which fixing a time window t, the impact of one time series into the other, represented by (${X}^{\left(t\right)}$), is summarized into entropy index (${Y}^{\left(t\right)}$). For each experiment period, the transfer entropy calculation was done per segmentation (time window), which transformed the exchanged information (causal direction) between one platform into a complexity measure value (entropy index). The entropy index calculated the causal effect from the right-side to the left-side of a time segment, as well the effect of the same time segment from the left-side to the right.

**Figure 3.**Visual summary of the methodological framework. Acquired data transformation was obtained by using the bi-dimensional time series related to the vertical platform (Fz) from the right and left side, then summarizing each piece of transference information as a complexity measure (using the transfer entropy). After summarizing these data into single values, they were compared using a Mixed-Effect Model (GLMM, Gamma Regression), as a longitudinal study. Then, analysis and conclusions were drawn.

**Figure 4.**Comparison across the Fz measurements from the platforms and dose-response on each montage. Panel

**A**represents the mean causal entropy from the left side to the right (L) and from the right side to the left (R). The black lines represent one standard error. Panel

**B**shows the transfer entropy of each platform per montage across the dose-response, time-invariant. Panel

**C**displays the nonlinear dynamic and complexity of each platform across time, smoothing the entropy trials evolution through a generalized additive model (GAM) and considering a confidence interval of 95%.

**Figure 5.**The left-hand plot shows the baseline entropy. The other plots show the time-varying pre/post stimulation entropy responses (off-line) across different montages, per side. The visualization adopted the LOESS smoothness, which shows that all montage presents equivalent mean entropy response regardless of its side (Left → Right represented by L, and Right → Left by R), therefore, showing no carryover effect.

**Figure 6.**Comparison of the entropy measurements across the different brain stimulation conditions, and the interaction effects between categorical predictors from the adjusted GLMM [26]. The results indicate that our sham protocol would not yield effects in our variable of interest and that the cathode center condition induced a significant effect on the postural control.

Stimuli Moment | Sham-Anodal | Sham-Cathodal | |
---|---|---|---|

Median [Range] | Median [Range] | ||

PRE or POST (OFF) | 0.0397 [0.02, 0.10] | 0.0438 [0.02, 0.10] | |

Right → Left | 1 mA (ON) | 0.0314 [0.01, 0.10] | 0.0429 [0.01, 0.13] |

2 mA (ON) | 0.0433 [0.02, 0.12] | 0.052 [0.01, 0.10] | |

3 mA (ON) | 0.0373 [0.02, 0.10] | 0.0481 [0.01, 0.09] | |

PRE or POST (OFF) | 0.0266 [0.01, 0.12] | 0.0396 [0.01, 0.12] | |

Left → Right | 1 mA (ON) | 0.0225 [0.01, 0.13] | 0.0393 [0.00, 0.16] |

2 mA (ON) | 0.0205 [0.01, 0.15] | 0.0667 [0.01, 0.13] | |

3 mA (ON) | 0.0206 [0.01, 0.11] | 0.0469 [0.00, 0.11] |

**Table 2.**Kruskal–Wallis tests considering each force place (information flux comes from the Left-side, Left → Right, or Right-side, Right → Left) across the difference between Sham-Anodal versus Sham-Cathodal stimulation observation per period.

p-Value | ||
---|---|---|

Stimuli Moment | Left-Side | Right-Side |

PRE or POST (OFF) | 0.529 | 0.505 |

1 mA (ON) | 0.791 | 0.375 |

2 mA (ON) | 0.099 | 0.214 |

3 mA (ON) | 0.255 | 0.408 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Favoretto, D.B.; Bergonzoni, E.; Nascimento, D.C.; Louzada, F.; Lemos, T.W.; Batistela, R.A.; Moraes, R.; Leite, J.P.; Rimoli, B.P.; Edwards, D.J.;
et al. High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial. *Appl. Sci.* **2022**, *12*, 2703.
https://doi.org/10.3390/app12052703

**AMA Style**

Favoretto DB, Bergonzoni E, Nascimento DC, Louzada F, Lemos TW, Batistela RA, Moraes R, Leite JP, Rimoli BP, Edwards DJ,
et al. High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial. *Applied Sciences*. 2022; 12(5):2703.
https://doi.org/10.3390/app12052703

**Chicago/Turabian Style**

Favoretto, Diandra B., Eduardo Bergonzoni, Diego Carvalho Nascimento, Francisco Louzada, Tenysson W. Lemos, Rosangela A. Batistela, Renato Moraes, João P. Leite, Brunna P. Rimoli, Dylan J. Edwards,
and et al. 2022. "High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial" *Applied Sciences* 12, no. 5: 2703.
https://doi.org/10.3390/app12052703