Skip to main content

Advertisement

SpringerLink
Log in

Impact of brain atrophy on tDCS and HD-tDCS current flow: a modeling study in three variants of primary progressive aphasia

  • Original Article
  • Published:
Neurological Sciences Aims and scope Submit manuscript

Abstract

Background

During transcranial direct current stimulation (tDCS), the amount and distribution of current that reaches the brain depends on individual anatomy. Many progressive neurodegenerative diseases are associated with cortical atrophy, but the importance of individual brain atrophy during tDCS in patients with progressive atrophy, including primary progressive aphasia (PPA), remains unclear.

Objective

In the present study, we addressed the question whether brain anatomy in patients with distinct cortical atrophy patterns would impact brain current intensity and distribution during tDCS over the left IFG.

Method

We developed state-of-the-art, gyri-precise models of three subjects, each representing a variant of primary progressive aphasia: non-fluent variant PPA (nfvPPA), semantic variant PPA (svPPA), and logopenic variant PPA (lvPPA). We considered two exemplary montages over the left inferior frontal gyrus (IFG): a conventional pad montage (anode over F7, cathode over the right cheek) and a 4 × 1 high-definition tDCS montage. We further considered whether local anatomical features, specifically distance of the cortex to skull, can directly predict local electric field intensity.

Results

We found that the differences in brain current flow across the three PPA variants fall within the distribution of anatomically typical adults. While clustering of electric fields was often around individual gyri or sulci, the minimal distance from the gyri/sulci to skull was not correlated with electric field intensity.

Conclusion

Limited to the conditions and assumptions considered here, this argues against a specific need to adjust the tDCS montage for these patients any more than might be considered useful in anatomically typical adults. Therefore, local atrophy does not, in isolation, reliably predict local electric field. Rather, our results are consistent with holistic head anatomy influencing brain current flow, with tDCS producing diffuse and individualized brain current flow patterns and HD-tDCS producing targeted brain current flow across individuals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Peterchev AV, Wagner TA, Miranda PC, Nitsche MA, Paulus W, Lisanby SH, Pascual-Leone A, Bikson M (2012) Fundamentals of transcranial electric and magnetic stimulation dose: definition, selection, and reporting practices. Brain Stimul 5:435–453. https://doi.org/10.1016/j.brs.2011.10.001

    Article  Google Scholar 

  2. Ruffini G, Wendling F, Merlet I, Molaee-Ardekani B, Mekonnen A, Salvador R, Soria-Frisch A, Grau C, Dunne S, Miranda PC (2013) Transcranial current brain stimulation (tCS): models and technologies. IEEE Trans Neural Syst Rehabil Eng 21:333–345. https://doi.org/10.1109/TNSRE.2012.2200046

    Article  Google Scholar 

  3. Miranda PC (2013) Physics of effects of transcranial brain stimulation. Handb Clin Neurol 116:353–366. https://doi.org/10.1016/B978-0-444-53497-2.00029-2

    Article  Google Scholar 

  4. Miranda PC, Lomarev M, Hallett M (2006) Modeling the current distribution during transcranial direct current stimulation. Clin Neurophysiol 117:1623–1629. https://doi.org/10.1016/j.clinph.2006.04.009

    Article  Google Scholar 

  5. Datta A, Bansal V, Diaz J, et al (2009) Gyri-precise head model of transcranial direct current stimulation: improved spatial focality using a ring electrode versus conventional rectangular pad. Brain Stimul 2:201–207, 207.e1

  6. Datta A, Truong D, Minhas P et al (2012) Inter-individual variation during transcranial direct current stimulation and normalization of dose using MRI-derived computational models. Front Psychiatry 3:91. https://doi.org/10.3389/fpsyt.2012.00091

    Article  Google Scholar 

  7. Parazzini M, Fiocchi S, Rossi E, Paglialonga A, Ravazzani P (2011) Transcranial direct current stimulation: estimation of the electric field and of the current density in an anatomical human head model. IEEE Trans Biomed Eng 58:1773–1780. https://doi.org/10.1109/TBME.2011.2116019

    Article  Google Scholar 

  8. Truong DQ, Magerowski G, Blackburn GL, Bikson M, Alonso-Alonso M (2013) Computational modeling of transcranial direct current stimulation (tDCS) in obesity: impact of head fat and dose guidelines. NeuroImage: Clinical 2:759–766. https://doi.org/10.1016/j.nicl.2013.05.011

    Article  Google Scholar 

  9. Opitz A, Paulus W, Will S, Antunes A, Thielscher A (2015) Determinants of the electric field during transcranial direct current stimulation. NeuroImage 109:140–150. https://doi.org/10.1016/j.neuroimage.2015.01.033

    Article  Google Scholar 

  10. Huang Y, Liu AA, Lafon B et al (2017) Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation. Elife 6. https://doi.org/10.7554/eLife.18834

  11. Jog MV, Smith RX, Jann K, Dunn W, Lafon B, Truong D, Wu A, Parra L, Bikson M, Wang DJ (2016) In-vivo imaging of magnetic fields induced by transcranial direct current stimulation (tDCS) in human brain using MRI. Sci Rep 6:34385. https://doi.org/10.1038/srep34385

    Article  CAS  Google Scholar 

  12. Datta A, Zhou X, Su Y, Parra LC, Bikson M (2013) Validation of finite element model of transcranial electrical stimulation using scalp potentials: implications for clinical dose. J Neural Eng 10:036018. https://doi.org/10.1088/1741-2560/10/3/036018

    Article  Google Scholar 

  13. Edwards D, Cortes M, Datta A, Minhas P, Wassermann EM, Bikson M (2013) Physiological and modeling evidence for focal transcranial electrical brain stimulation in humans: a basis for high-definition tDCS. NeuroImage 74:266–275. https://doi.org/10.1016/j.neuroimage.2013.01.042

    Article  Google Scholar 

  14. Datta A, Bikson M, Fregni F (2010) Transcranial direct current stimulation in patients with skull defects and skull plates: high-resolution computational FEM study of factors altering cortical current flow. Neuroimage 52:1268–1278

    Article  Google Scholar 

  15. Tomio R, Akiyama T, Horikoshi T et al (2015) Visualization of the electric field evoked by transcranial electric stimulation during a craniotomy using the finite element method. J Neurosci Methods 256:157–167. https://doi.org/10.1016/j.jneumeth.2015.09.014

    Article  Google Scholar 

  16. Gillick BT, Kirton A, Carmel JB, Minhas P, Bikson M (2014) Pediatric stroke and transcranial direct current stimulation: methods for rational individualized dose optimization. Front Hum Neurosci 8:739. https://doi.org/10.3389/fnhum.2014.00739

    Article  Google Scholar 

  17. Dmochowski JP, Datta A, Huang Y, Richardson JD, Bikson M, Fridriksson J, Parra LC (2013) Targeted transcranial direct current stimulation for rehabilitation after stroke. Neuroimage 75:12–19. https://doi.org/10.1016/j.neuroimage.2013.02.049

    Article  Google Scholar 

  18. Datta A, Baker JM, Bikson M, Fridriksson J (2011) Individualized model predicts brain current flow during transcranial direct-current stimulation treatment in responsive stroke patient. Brain Stimulation 4:169–174. https://doi.org/10.1016/j.brs.2010.11.001

    Article  Google Scholar 

  19. Galletta EE, Cancelli A, Cottone C, Simonelli I, Tecchio F, Bikson M, Marangolo P (2015) Use of computational modeling to inform tDCS electrode montages for the promotion of language recovery in post-stroke aphasia. Brain Stimul 8:1108–1115. https://doi.org/10.1016/j.brs.2015.06.018

    Article  Google Scholar 

  20. Ciechanski P, Carlson HL, Yu SS, Kirton A (2018) Modeling transcranial direct-current stimulation-induced electric fields in children and adults. Front Hum Neurosci 12:268. https://doi.org/10.3389/fnhum.2018.00268

    Article  Google Scholar 

  21. Minhas P, Bikson M, Woods AJ, et al (2012) Transcranial direct current stimulation in pediatric brain: a computational modeling study. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Pp 859–862

  22. Teichmann M, Lesoil C, Godard J, Vernet M, Bertrand A, Levy R, Dubois B, Lemoine L, Truong DQ, Bikson M, Kas A, Valero-Cabré A (2016) Direct current stimulation over the anterior temporal areas boosts semantic processing in primary progressive aphasia. Ann Neurol 80:693–707. https://doi.org/10.1002/ana.24766

    Article  CAS  Google Scholar 

  23. Fridriksson J, den Ouden D-B, Hillis AE et al (2018) Anatomy of aphasia revisited. Brain 141:848–862

    Article  Google Scholar 

  24. Tsapkini K, Webster KT, Ficek BN, Desmond JE, Onyike CU, Rapp B, Frangakis CE, Hillis AE (2018) Electrical brain stimulation in different variants of primary progressive aphasia: a randomized clinical trial. Alzheimers Dement (N Y) 4:461–472. https://doi.org/10.1016/j.trci.2018.08.002

    Article  Google Scholar 

  25. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, Ogar JM, Rohrer JD, Black S, Boeve BF, Manes F, Dronkers NF, Vandenberghe R, Rascovsky K, Patterson K, Miller BL, Knopman DS, Hodges JR, Mesulam MM, Grossman M (2011) Classification of primary progressive aphasia and its variants. Neurology 76:1006–1014

    Article  Google Scholar 

  26. Mesulam M, Wieneke C, Rogalski E, Cobia D, Thompson C, Weintraub S (2009) Quantitative template for subtyping primary progressive aphasia. Arch Neurol 66:1545–1551

    Article  Google Scholar 

  27. Cotelli M, Manenti R, Paternicò D, Cosseddu M, Brambilla M, Petesi M, Premi E, Gasparotti R, Zanetti O, Padovani A, Borroni B (2016) Grey matter density predicts the improvement of naming abilities after tDCS intervention in agrammatic variant of primary progressive aphasia. Brain Topogr 29:738–751

    Article  Google Scholar 

  28. de Aguiar V, Zhao Y, Faria A, et al (under review) Brain volumes as predictors of electrical stimulation effects in primary progressive aphasia

  29. Datta A, Elwassif M, Battaglia F, Bikson M (2008) Transcranial current stimulation focality using disc and ring electrode configurations: FEM analysis. J Neural Eng 5:163–174. https://doi.org/10.1088/1741-2560/5/2/007

    Article  Google Scholar 

  30. Huang Y, Datta A, Bikson M, Parra LC (2017) Realistic volumetric-approach to simulate transcranial electric stimulation -- ROAST -- a fully automated open-source pipeline bioRxiv 217331. https://doi.org/10.1101/217331

  31. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851. https://doi.org/10.1016/j.neuroimage.2005.02.018

    Article  Google Scholar 

  32. Huang Y, Dmochowski JP, Su Y, Datta A, Rorden C, Parra LC (2013) Automated MRI segmentation for individualized modeling of current flow in the human head. J Neural Eng 10:066004. https://doi.org/10.1088/1741-2560/10/6/066004

    Article  Google Scholar 

  33. Ghosh S, Moorthy S (1995) Elastic-plastic analysis of arbitrary heterogeneous materials with the Voronoi cell finite element method. Comput Methods Appl Mech Eng 121:373–409. https://doi.org/10.1016/0045-7825(94)00687-I

  34. Knopman DS, Kramer JH, Boeve BF, Caselli RJ, Graff-Radford NR, Mendez MF, Miller BL, Mercaldo N (2008) Development of methodology for conducting clinical trials in frontotemporal lobar degeneration. Brain 131:2957–2968

    Article  Google Scholar 

  35. Seo H, Kim D, Jun SC (2015) Computational study of subdural cortical stimulation: effects of simulating anisotropic conductivity on activation of cortical neurons. PLoS One 10:e0128590

    Article  Google Scholar 

  36. Waters S, Wiestler T, Diedrichsen J (2017) Cooperation not competition: bihemispheric tDCS and fMRI show role for ipsilateral hemisphere in motor learning. J Neurosci 37:7500–7512

    Article  CAS  Google Scholar 

  37. Petrov PI, Mandija S, Sommer IE et al (2017) How much detail is needed in modeling a transcranial magnetic stimulation figure-8 coil: measurements and brain simulations. PLoS One 12:e0178952

    Article  Google Scholar 

  38. Bonaiuto JJ, de Berker A, Bestmann S (2016) Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models. Elife 5:e20047

    Article  Google Scholar 

  39. Lafon B, Rahman A, Bikson M, Parra LC (2017) Direct current stimulation alters neuronal input/output function. Brain Stimul 10:36–45. https://doi.org/10.1016/j.brs.2016.08.014

    Article  Google Scholar 

  40. Laakso I, Tanaka S, Koyama S et al (2015) Inter-subject variability in electric fields of motor cortical tDCS. Brain Stimulation. https://doi.org/10.1016/j.brs.2015.05.002

  41. Rawji V, Ciocca M, Zacharia A, Soares D, Truong D, Bikson M, Rothwell J, Bestmann S (2018) tDCS changes in motor excitability are specific to orientation of current flow. Brain Stimul 11:289–298. https://doi.org/10.1016/j.brs.2017.11.001

    Article  Google Scholar 

  42. Salvador R, Wenger C, Miranda PC (2015) Investigating the cortical regions involved in MEP modulation in tDCS Front Cell Neurosci 9:. https://doi.org/10.3389/fncel.2015.00405

  43. Mikkonen M, Laakso I, Sumiya M, Koyama S, Hirata A, Tanaka S (2018) TMS motor thresholds correlate with TDCS electric field strengths in hand motor area. Front Neurosci 12:426. https://doi.org/10.3389/fnins.2018.00426

    Article  Google Scholar 

  44. Rahman A, Reato D, Arlotti M, Gasca F, Datta A, Parra LC, Bikson M (2013) Cellular effects of acute direct current stimulation: somatic and synaptic terminal effects. J Physiol 591:2563–2578. https://doi.org/10.1113/jphysiol.2012.247171

    Article  CAS  Google Scholar 

  45. Kabakov AY, Muller PA, Pascual-Leone A, Jensen FE, Rotenberg A (2012) Contribution of axonal orientation to pathway-dependent modulation of excitatory transmission by direct current stimulation in isolated rat hippocampus. J Neurophysiol 107:1881–1889. https://doi.org/10.1152/jn.00715.2011

    Article  Google Scholar 

  46. Dmochowski JP, Datta A, Bikson M, Su Y, Parra LC (2011) Optimized multi-electrode stimulation increases focality and intensity at target. J Neural Eng 8:046011. https://doi.org/10.1088/1741-2560/8/4/046011

    Article  Google Scholar 

  47. Miranda PC, Salvador R, Wenger C, Fernandes SR (2017) Optimizing electric-field delivery for tDCS: virtual humans help to design efficient, noninvasive brain and spinal cord electrical stimulation. IEEE Pulse 8:42–45. https://doi.org/10.1109/MPUL.2017.2701259

    Article  Google Scholar 

  48. Bikson M, Dmochowski J, Rahman A (2013) The “quasi-uniform” assumption in animal and computational models of non-invasive electrical stimulation. Brain Stimul 6:704–705. https://doi.org/10.1016/j.brs.2012.11.005

    Article  Google Scholar 

  49. Bikson M, Truong DQ, Mourdoukoutas AP, et al (2015) Chapter 1 - Modeling sequence and quasi-uniform assumption in computational neurostimulation. In: Bestmann S (ed) Progress in Brain Research. Elsevier, pp 1–23

  50. Wagner S, Rampersad SM, Aydin Ü, Vorwerk J, Oostendorp TF, Neuling T, Herrmann CS, Stegeman DF, Wolters CH (2014) Investigation of tDCS volume conduction effects in a highly realistic head model. J Neural Eng 11:016002. https://doi.org/10.1088/1741-2560/11/1/016002

    Article  CAS  Google Scholar 

  51. Huang Y, Datta A, Bikson M, Parra LC (2018) ROAST: an open-source, fully-automated, realistic volumetric-approach-based simulator for TES. Conf Proc IEEE Eng Med Biol Soc 2018:3072–3075. https://doi.org/10.1109/EMBC.2018.8513086

    Article  Google Scholar 

  52. Thielscher A, Antunes A, Saturnino GB (2015) Field modeling for transcranial magnetic stimulation: a useful tool to understand the physiological effects of TMS? In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Pp 222–225

  53. Truong DQ, Hüber M, Xie X, Datta A, Rahman A, Parra LC, Dmochowski JP, Bikson M (2014) Clinician accessible tools for GUI computational models of transcranial electrical stimulation: BONSAI and SPHERES. Brain Stimul 7:521–524. https://doi.org/10.1016/j.brs.2014.03.009

    Article  Google Scholar 

  54. Shahid SS, Bikson M, Salman H, Wen P, Ahfock T (2014) The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation. J Neural Eng 11:036002. https://doi.org/10.1088/1741-2560/11/3/036002

    Article  Google Scholar 

  55. Bikson M, Datta A (2012) Guidelines for precise and accurate computational models of tDCS. Brain Stimulation 5:430–431. https://doi.org/10.1016/j.brs.2011.06.001

    Article  Google Scholar 

  56. Santos L, Martinho M, Salvador R et al (2016) Evaluation of the electric field in the brain during transcranial direct current stimulation: a sensitivity analysis. Conf Proc IEEE Eng Med Biol Soc 2016:1778–1781. https://doi.org/10.1109/EMBC.2016.7591062

    Article  Google Scholar 

  57. Miranda PC, Salvador R, Wenger C, Fernandes SR (2016) Computational models of non-invasive brain and spinal cord stimulation. Conf Proc IEEE Eng Med Biol Soc 2016:6457–6460. https://doi.org/10.1109/EMBC.2016.7592207

    Article  Google Scholar 

  58. de Berker AO, Bikson M, Bestmann S (2013) Predicting the behavioral impact of transcranial direct current stimulation: issues and limitations. Front Hum Neurosci 7:613. https://doi.org/10.3389/fnhum.2013.00613

    Article  Google Scholar 

  59. Bestmann S, Ward N (2017) Are current flow models for transcranial electrical stimulation fit for purpose? Brain Stimul 10:865–866. https://doi.org/10.1016/j.brs.2017.04.002

    Article  Google Scholar 

  60. Molaee-Ardekani B, Márquez-Ruiz J, Merlet I et al (2013) Effects of transcranial direct current stimulation (tDCS) on cortical activity: a computational modeling study. Brain Stimulation 6:25–39. https://doi.org/10.1016/j.brs.2011.12.006

    Article  Google Scholar 

  61. Bonaiuto JJ, Bestmann S (2015) Understanding the nonlinear physiological and behavioral effects of tDCS through computational neurostimulation. Prog Brain Res 222:75–103. https://doi.org/10.1016/bs.pbr.2015.06.013

    Article  Google Scholar 

  62. Esmaeilpour Z, Marangolo P, Hampstead BM, Bestmann S, Galletta E, Knotkova H, Bikson M (2018) Incomplete evidence that increasing current intensity of tDCS boosts outcomes. Brain Stimul 11:310–321. https://doi.org/10.1016/j.brs.2017.12.002

    Article  Google Scholar 

  63. Kuo H-I, Bikson M, Datta A, Minhas P, Paulus W, Kuo MF, Nitsche MA (2013) Comparing cortical plasticity induced by conventional and high-definition 4 × 1 ring tDCS: a neurophysiological study. Brain Stimulation 6:644–648. https://doi.org/10.1016/j.brs.2012.09.010

    Article  Google Scholar 

  64. de Aguiar V, Zhao Y, Ficek B, et al (under review) Predictors of the effects of spelling intervention and tDCS in primary progressive aphasia

  65. Ficek BN, Wang Z, Zhao Y, et al (2018) The effect of tDCS on functional connectivity in primary progressive aphasia. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2018.05.023

  66. Mandelli ML, Welch AE, Vilaplana E, Watson C, Battistella G, Brown JA, Possin KL, Hubbard HI, Miller ZA, Henry ML, Marx GA, Santos-Santos MA, Bajorek LP, Fortea J, Boxer A, Rabinovici G, Lee S, Deleon J, Rosen HJ, Miller BL, Seeley WW, Gorno-Tempini ML (2018) Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA. Cortex 108:252–264. https://doi.org/10.1016/j.cortex.2018.08.002

    Article  Google Scholar 

  67. Petrides M, Alivisatos B, Evans AC (1995) Functional activation of the human ventrolateral frontal cortex during mnemonic retrieval of verbal information. Proc Natl Acad Sci U S A 92:5803–5807

    Article  CAS  Google Scholar 

  68. Thompson-Schill SL, D’Esposito M, Aguirre GK, Farah MJ (1997) Role of left inferior prefrontal cortex in retrieval of semantic knowledge: a reevaluation. Proc Natl Acad Sci 94:14792–14797

    Article  CAS  Google Scholar 

  69. Benton AL, Sivan AB, deS Hamsher K, Spreen O (1994) Contributions to neuropsychological assessment: a clinical manual. Oxford University Press, USA

  70. Wechsler D (1981) Manual for the Wechsler adult intelligence scale-revised (WAIS-R). San Antonio, TX: The Psychological Corporation

  71. Kaplan E (1983) The assessment of aphasia and related disorders. Lippincott Williams & Wilkins

  72. Breining BL, Tippett DC, Posner J, et al (2015) Assessing dissociations of object and action naming in acute stroke. In: Clinical Aphasiology Conference, Monterey, CA

  73. Love T, Oster E (2002) On the categorization of aphasic typologies: the SOAP (a test of syntactic complexity). J Psycholinguist Res 31:503–529. https://doi.org/10.1023/A:1021208903394

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank our participants and referring physicians for their dedication and interest in our study.

Funding

This work was supported through the National Institutes of Health: NIH–NINDS 1R01NS101362, NIH–NIMH 1R01MH111896 to MB and by the National Institutes of Health (National Institute of Deafness and Communication Disorders) through award R01 DC014475 to KT.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, The City College of New York, New York, NY, 10031, USA

    Gozde Unal, Syed Shahabuddin, Dennis Truong & Marom Bikson

  2. Department of Neurology, Cerebrovascular Division, Johns Hopkins Medicine, 600 N. Wolfe Street, Phipps 488, Baltimore, MD, 21287, USA

    Bronte Ficek, Kimberly Webster & Kyrana Tsapkini

  3. Department of Otolaryngology, Johns Hopkins Medicine, Baltimore, MD, 21287, USA

    Kimberly Webster

  4. Department of Cognitive Science, Johns Hopkins Medicine, Baltimore, MD, 21218, USA

    Kyrana Tsapkini

  5. Department of Psychiatry, University of Michigan, Ann Arbor, MI, 48109, USA

    Benjamin Hampstead

Authors
  1. Gozde Unal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bronte Ficek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kimberly Webster
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Syed Shahabuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dennis Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benjamin Hampstead
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marom Bikson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kyrana Tsapkini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyrana Tsapkini.

Ethics declarations

Conflict of interest

The City University of New York has patents on Brain Stimulation with MB as inventor. MB has equity in Soterix Medical and serves on the Boston Scientific and GlaxoSmithKline scientific advisory boards.

Ethical approval

The study was approved by Johns Hopkins University IRB.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Unal, G., Ficek, B., Webster, K. et al. Impact of brain atrophy on tDCS and HD-tDCS current flow: a modeling study in three variants of primary progressive aphasia. Neurol Sci 41, 1781–1789 (2020). https://doi.org/10.1007/s10072-019-04229-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10072-019-04229-z

Keywords

Search

Navigation