301 related articles for article (PubMed ID: 31071686)
1. Realistic volumetric-approach to simulate transcranial electric stimulation-ROAST-a fully automated open-source pipeline.
Huang Y; Datta A; Bikson M; Parra LC
J Neural Eng; 2019 Jul; 16(5):056006. PubMed ID: 31071686
[TBL] [Abstract][Full Text] [Related]
2. ROAST: An Open-Source, Fully-Automated, Realistic Volumetric-Approach-Based Simulator For TES.
Huang Y; Datta A; Bikson M; Parra LC
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():3072-3075. PubMed ID: 30441043
[TBL] [Abstract][Full Text] [Related]
3. Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art.
Nielsen JD; Madsen KH; Puonti O; Siebner HR; Bauer C; Madsen CG; Saturnino GB; Thielscher A
Neuroimage; 2018 Jul; 174():587-598. PubMed ID: 29518567
[TBL] [Abstract][Full Text] [Related]
4. Automated MRI segmentation for individualized modeling of current flow in the human head.
Huang Y; Dmochowski JP; Su Y; Datta A; Rorden C; Parra LC
J Neural Eng; 2013 Dec; 10(6):066004. PubMed ID: 24099977
[TBL] [Abstract][Full Text] [Related]
5. The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting.
Huang Y; Parra LC; Haufe S
Neuroimage; 2016 Oct; 140():150-62. PubMed ID: 26706450
[TBL] [Abstract][Full Text] [Related]
6. Applications of open-source software ROAST in clinical studies: A review.
Nasimova M; Huang Y
Brain Stimul; 2022; 15(4):1002-1010. PubMed ID: 35843597
[TBL] [Abstract][Full Text] [Related]
7. A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains.
Kalloch B; Bazin PL; Villringer A; Sehm B; Hlawitschka M
PLoS One; 2020; 15(5):e0228119. PubMed ID: 32407389
[TBL] [Abstract][Full Text] [Related]
8. Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.
Puonti O; Van Leemput K; Saturnino GB; Siebner HR; Madsen KH; Thielscher A
Neuroimage; 2020 Oct; 219():117044. PubMed ID: 32534963
[TBL] [Abstract][Full Text] [Related]
9. Comparative performance of the finite element method and the boundary element fast multipole method for problems mimicking transcranial magnetic stimulation (TMS).
Htet AT; Saturnino GB; Burnham EH; Noetscher GM; Nummenmaa A; Makarov SN
J Neural Eng; 2019 Apr; 16(2):024001. PubMed ID: 30605893
[TBL] [Abstract][Full Text] [Related]
10. Electric field calculations in brain stimulation based on finite elements: an optimized processing pipeline for the generation and usage of accurate individual head models.
Windhoff M; Opitz A; Thielscher A
Hum Brain Mapp; 2013 Apr; 34(4):923-35. PubMed ID: 22109746
[TBL] [Abstract][Full Text] [Related]
11. A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation.
Goswami N; Shen M; Gomez LJ; Dannhauer M; Sommer MA; Peterchev AV
J Neurosci Methods; 2024 Aug; 408():110176. PubMed ID: 38795980
[TBL] [Abstract][Full Text] [Related]
12. Electric field simulations for transcranial brain stimulation using FEM: an efficient implementation and error analysis.
Saturnino GB; Madsen KH; Thielscher A
J Neural Eng; 2019 Nov; 16(6):066032. PubMed ID: 31487695
[TBL] [Abstract][Full Text] [Related]
13. Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES.
Rashed EA; Gomez-Tames J; Hirata A
Phys Med Biol; 2021 Mar; 66(6):064002. PubMed ID: 33524957
[TBL] [Abstract][Full Text] [Related]
14. A simple method for EEG guided transcranial electrical stimulation without models.
Cancelli A; Cottone C; Tecchio F; Truong DQ; Dmochowski J; Bikson M
J Neural Eng; 2016 Jun; 13(3):036022. PubMed ID: 27172063
[TBL] [Abstract][Full Text] [Related]
15. Benchmarking transcranial electrical stimulation finite element models: a comparison study.
Indahlastari A; Chauhan M; Sadleir RJ
J Neural Eng; 2019 Apr; 16(2):026019. PubMed ID: 30605892
[TBL] [Abstract][Full Text] [Related]
16. On the importance of using both T1-weighted and T2-weighted structural magnetic resonance imaging scans to model electric fields induced by non-invasive brain stimulation in SimNIBS.
Van Hoornweder S; Meesen R; Caulfield KA
Brain Stimul; 2022; 15(3):641-644. PubMed ID: 35436593
[No Abstract] [Full Text] [Related]
17. Inter-individual and age-dependent variability in simulated electric fields induced by conventional transcranial electrical stimulation.
Antonenko D; Grittner U; Saturnino G; Nierhaus T; Thielscher A; Flöel A
Neuroimage; 2021 Jan; 224():117413. PubMed ID: 33011418
[TBL] [Abstract][Full Text] [Related]
18. An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data.
Schirner M; Rothmeier S; Jirsa VK; McIntosh AR; Ritter P
Neuroimage; 2015 Aug; 117():343-57. PubMed ID: 25837600
[TBL] [Abstract][Full Text] [Related]
19. The effect of meninges on the electric fields in TES and TMS. Numerical modeling with adaptive mesh refinement.
Weise K; Wartman WA; Knösche TR; Nummenmaa AR; Makarov SN
Brain Stimul; 2022; 15(3):654-663. PubMed ID: 35447379
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
