references.bib

@article{10.1063/5.0160334,
  author        = {Gjerde, I. G. and Rognes, M. E. and S\'{a}nchez, A. L.},
  title         = {The directional flow generated by peristalsis in perivascular networks--Theoretical and numerical reduced-order descriptions},
  journal       = {Journal of Applied Physics},
  volume        = {134},
  number        = {17},
  pages         = {174701},
  year          = {2023},
  month         = {11},
  abstract      = {Directional fluid flow in perivascular spaces surrounding cerebral arteries is hypothesized to play a key role in brain solute transport and clearance. While various drivers for a pulsatile flow, such as cardiac or respiratory pulsations, are well quantified, the question remains as to which mechanisms could induce a directional flow within physiological regimes. To address this question, we develop theoretical and numerical reduced-order models to quantify the directional (net) flow induceable by peristaltic pumping in periarterial networks. Each periarterial element is modeled as a slender annular space bounded internally by a circular tube supporting a periodic traveling (peristaltic) wave. Under reasonable assumptions of a small Reynolds number flow, small radii, and small-amplitude peristaltic waves, we use lubrication theory and regular perturbation methods to derive theoretical expressions for the directional net flow and pressure distribution in the perivascular network. The reduced model is used to derive closed-form analytical expressions for the net flow for simple network configurations of interest, including single elements, two elements in tandem, and a three element bifurcation, with results compared with numerical predictions. In particular, we provide a computable theoretical estimate of the net flow induced by peristaltic motion in perivascular networks as a function of physiological parameters, notably, wave length, frequency, amplitude, and perivascular dimensions. Quantifying the maximal net flow for specific physiological regimes, we find that vasomotion may induce net pial periarterial flow velocities on the order of a few to tens of   \ensuremath{\mu}m/s and that sleep-related changes in vasomotion pulsatility may drive a threefold flow increase.},
  issn          = {0021-8979},
  doi           = {10.1063/5.0160334},
  url           = {https://doi.org/10.1063/5.0160334},
  eprint        = {https://pubs.aip.org/aip/jap/article-pdf/doi/10.1063/5.0160334/18195883/174701\_1\_5.0160334.pdf}
}
@article{10.1088/2057-1976/ad7268,
  author        = {Finsberg, Henrik Nicolay Topnes and Charwat, Verena and Healy, Kevin E and Wall, Samuel},
  title         = {Automatic motion estimation with applications to hiPSC-CMs},
  journal       = {Biomedical Physics \& Engineering Express},
  url           = {http://iopscience.iop.org/article/10.1088/2057-1976/ad7268},
  year          = {2024}
}
@article{arostica2025117485,
  title         = {A software benchmark for cardiac elastodynamics},
  journal       = {Computer Methods in Applied Mechanics and Engineering},
  volume        = {435},
  pages         = {117485},
  year          = {2025},
  issn          = {0045-7825},
  doi           = {https://doi.org/10.1016/j.cma.2024.117485},
  url           = {https://www.sciencedirect.com/science/article/pii/S0045782524007394},
  author        = {Reidmen Ar\'{o}stica and David Nolte and Aaron Brown and Amadeus Gebauer and Elias Karabelas and Javiera Jilberto and Matteo Salvador and Michele Bucelli and Roberto Piersanti and Kasra Osouli and Christoph Augustin and Henrik Finsberg and Lei Shi and Marc Hirschvogel and Martin Pfaller and Pasquale Claudio Africa and Matthias Gsell and Alison Marsden and David Nordsletten and Francesco Regazzoni and Gernot Plank and Joakim Sundnes and Luca Dede' and Mathias Peirlinck and Vijay Vedula and Wolfgang Wall and Crist\'{o}bal Bertoglio},
  keywords      = {Cardiac mechanics, Software, Finite elements, Benchmark, Hyperelasticity},
  abstract      = {In cardiovascular mechanics, reaching consensus in simulation results within a physiologically relevant range of parameters is essential for reproducibility purposes. Although currently available benchmarks contain some of the features that cardiac mechanics models typically include, some important modeling aspects are missing. Therefore, we propose a new set of cardiac benchmark problems and solutions for assessing passive and active material behavior, viscous effects, and pericardial boundary condition. The problems proposed include simplified analytical fiber definitions and active stress models on a monoventricular and biventricular domains, allowing straightforward testing and validation with already developed solvers.}
}
@misc{budisa2022,
  doi           = {10.48550/ARXIV.2210.13274},
  url           = {https://arxiv.org/abs/2210.13274},
  author        = {Budisa, Ana and Hu, Xiaozhe and Kuchta, Miroslav and Mardal, Kent-Andre and Zikatanov, Ludmil},
  keywords      = {Numerical Analysis (math.NA), FOS: Mathematics, FOS: Mathematics, G.1.8; G.4, 65-04, 65N55, 65F08, 65H10},
  title         = {{HAZniCS -- Software Components for Multiphysics Problems}},
  publisher     = {arXiv},
  year          = {2022},
  copyright     = {arXiv.org perpetual, non-exclusive license}
}
@article{causemann2022,
  author        = {Causemann, Marius and Vinje, Vegard and Rognes, Marie E.},
  title         = {Human intracranial pulsatility during the cardiac cycle: a computational modelling framework},
  elocation-id  = {2022.05.19.492650},
  year          = {2022},
  doi           = {10.1101/2022.05.19.492650},
  publisher     = {Cold Spring Harbor Laboratory},
  journal       = {bioRxiv}
}
@article{daversin2022,
  author        = {Daversin-Catty, C\'{e}cile and Gjerde, Ingeborg G. and Rognes, Marie E.},
  title         = {{Geometrically Reduced Modelling of Pulsatile Flow in Perivascular Networks}},
  journal       = {Frontiers in Physics},
  volume        = {10},
  year          = {2022},
  doi           = {10.3389/fphy.2022.882260},
  issn          = {2296-424X}
}
@article{dokken2024adios,
  doi           = {10.21105/joss.06451},
  year          = {2024},
  publisher     = {The Open Journal},
  volume        = {9},
  number        = {96},
  pages         = {6451},
  author        = {J\o{}rgen Schartum Dokken},
  title         = {{ADIOS4DOLFINx: A framework for checkpointing in FEniCS}},
  journal       = {Journal of Open Source Software}
}
@article{finsberg2023,
  doi           = {10.21105/joss.04753},
  url           = {https://doi.org/10.21105/joss.04753},
  year          = {2023},
  publisher     = {The Open Journal},
  volume        = {8},
  number        = {81},
  pages         = {4753},
  author        = {Henrik Nicolay Topnes Finsberg and Ilsbeth Gerarda Maria van Herck and C\'{e}cile Daversin-Catty and Hermenegild Arevalo and Samuel Wall},
  title         = {simcardems: A FEniCS-based cardiac electro-mechanics solver},
  journal       = {Journal of Open Source Software}
}
@article{finsberg2024,
  doi           = {10.21105/joss.07063},
  url           = {https://doi.org/10.21105/joss.07063},
  year          = {2024},
  publisher     = {The Open Journal},
  volume        = {9},
  number        = {102},
  pages         = {7063},
  author        = {Henrik Finsberg and Johan Hake},
  title         = {gotranx: General ODE translator},
  journal       = {Journal of Open Source Software}
}
@article{gjerde2022,
  title         = {Nitsche's method for Navier--Stokes equations with slip boundary conditions},
  author        = {Gjerde, Ingeborg and Scott, L},
  journal       = {Mathematics of Computation},
  volume        = {91},
  number        = {334},
  pages         = {597--622},
  year          = {2022},
  doi           = {10.1090/mcom/3682}
}
@article{haubner2023,
  author        = {Haubner, Johannes and Neumann, Franziska and Ulbrich, Michael},
  title         = {{A Novel Density Based Approach for Topology Optimization of Stokes Flow}},
  journal       = {SIAM Journal on Scientific Computing},
  volume        = {45},
  number        = {2},
  pages         = {A338-A368},
  year          = {2023},
  doi           = {10.1137/21M143114X}
}
@article{https://doi.org/10.1002/cnm.2982,
  author        = {Finsberg, Henrik and Xi, Ce and Tan, Ju Le and Zhong, Liang and Genet, Martin and Sundnes, Joakim and Lee, Lik Chuan and Wall, Samuel T.},
  title         = {Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization},
  journal       = {International Journal for Numerical Methods in Biomedical Engineering},
  volume        = {34},
  number        = {7},
  pages         = {e2982},
  keywords      = {cardiac mechanics, contractility estimation, data assimilation, parameter estimation, patient specific simulations, stress estimation},
  doi           = {https://doi.org/10.1002/cnm.2982},
  url           = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.2982},
  eprint        = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cnm.2982},
  note          = {e2982 cnm.2982},
  abstract      = {Abstract Individually personalized computational models of heart mechanics can be used to estimate important physiological and clinically-relevant quantities that are difficult, if not impossible, to directly measure in the beating heart. Here, we present a novel and efficient framework for creating patient-specific biventricular models using a gradient-based data assimilation method for evaluating regional myocardial contractility and estimating myofiber stress. These simulations can be performed on a regular laptop in less than 2~h and produce excellent fit between measured and simulated volume and strain data through the entire cardiac cycle. By applying the framework using data obtained from 3 healthy human biventricles, we extracted clinically important quantities as well as explored the role of fiber angles on heart function. Our results show that steep fiber angles at the endocardium and epicardium are required to produce simulated motion compatible with measured strain and volume data. We also find that the contraction and subsequent systolic stresses in the right ventricle are significantly lower than that in the left ventricle. Variability of the estimated quantities with respect to both patient data and modeling choices are also found to be low. Because of its high efficiency, this framework may be applicable to modeling of patient specific cardiac mechanics for diagnostic purposes.},
  year          = {2018}
}
@article{laughlin2023smart,
  doi           = {10.21105/joss.05580},
  year          = {2023},
  publisher     = {The Open Journal},
  volume        = {8},
  number        = {90},
  pages         = {5580},
  author        = {Justin G. Laughlin and J\o{}rgen S. Dokken and Henrik N.t. Finsberg and Emmet A. Francis and Christopher T. Lee and Marie E. Rognes and Padmini Rangamani},
  title         = {{SMART: Spatial Modeling Algorithms for Reactions and Transport}},
  journal       = {Journal of Open Source Software}
}
@article{LUNSONGA202599,
  title         = {The sodium/glucose cotransporter 2 inhibitor Empagliflozin inhibits long QT 3 late sodium currents in a mutation specific manner},
  journal       = {Journal of Molecular and Cellular Cardiology},
  volume        = {198},
  pages         = {99--111},
  year          = {2025},
  issn          = {0022-2828},
  doi           = {https://doi.org/10.1016/j.yjmcc.2024.11.014},
  url           = {https://www.sciencedirect.com/science/article/pii/S0022282824002049},
  author        = {Lynn C. Lunsonga and Mohammad Fatehi and Wentong Long and Amy J. Barr and Brittany Gruber and Arkapravo Chattopadhyay and Khaled Barakat and Andrew G. Edwards and Peter E. Light},
  keywords      = {Empagliflozin, Long QT syndrome type 3, Late sodium current, Nav1.5, Arrhythmia, Cardioprotection},
  abstract      = {Background Sodium/glucose cotransporter 2 inhibitors (SGLT2is) like empagliflozin have demonstrated cardioprotective effects in patients with or without diabetes. SGLT2is have been shown to selectively inhibit the late component of cardiac sodium current (late INa). Induction of late INa is the primary mechanism in the pathophysiology of congenital long QT syndrome type 3 (LQT3) gain-of-function mutations in the SCN5A gene encoding Nav1.5. We investigated empagliflozin's effect on late INa in thirteen known LQT3 mutations located in distinct regions of the channel. Methods The whole-cell patch-clamp technique was used to investigate the effect of empagliflozin on late INa in recombinantly expressed Nav1.5 channels containing different LQT3 mutations. Molecular modeling of human Nav1.5 and simulations in a mathematical model of human ventricular myocytes were used to extrapolate our experimental results to excitation-contraction coupling. Results Empagliflozin selectively inhibited late INa in LQT3 mutations in the inactivation gate region of Nav1.5, without affecting peak current or channel kinetics. In contrast, empagliflozin inhibited both peak and late INa in mutations in the S4 voltage-sensing regions, altered channel gating, and slowed recovery from inactivation. Empagliflozin had no effect on late/peak INa or channel kinetics in channels with mutations in the putative empagliflozin binding region. Simulation results predict that empagliflozin may have a desirable therapeutic effect in LQT3 mutations in the inactivation gate region. Conclusions Empagliflozin selectively inhibits late INa, without affecting channel kinetics, in LQT3 mutations in the inactivation gate region. Empagliflozin may thus be a promising precision medicine approach for patients with specific LQT3 mutations.}
}
@inproceedings{monopoli2025arrhythmic,
  title         = {Arrhythmic Mitral Valve Syndrome: Insights from Left Ventricular End-Systolic Shape Analysis},
  author        = {Monopoli, Giulia and Sadeghinia, Mohammad Javad and Westrum Aabel, Eivind and Ribe, Margareth and Castrini, Anna Isotta and Hasselberg, Nina and Bugge, Cecilie and Five, Christian and Haugaa, Kristina and Balaban, Gabriel and others},
  booktitle     = {International Conference on Functional Imaging and Modeling of the Heart},
  pages         = {26--36},
  year          = {2025},
  organization  = {Springer}
}
@article{monopoli2025deepvalve,
  title         = {DeepValve: The first automatic detection pipeline for the mitral valve in Cardiac Magnetic Resonance imaging},
  author        = {Monopoli, Giulia and Haas, Daniel and Singh, Ashay and Aabel, Eivind Westrum and Ribe, Margareth and Castrini, Anna Isotta and Hasselberg, Nina Eide and Bugge, Cecilie and Five, Christian and Haugaa, Kristina and others},
  journal       = {Computers in Biology and Medicine},
  volume        = {192},
  pages         = {110211},
  year          = {2025},
  publisher     = {Elsevier}
}
@article{odeigah2024computational,
  title         = {A computational study of right ventricular mechanics in a rat model of pulmonary arterial hypertension},
  author        = {Odeigah, Oscar O and Kwan, Ethan D and Garcia, Kristen M and Finsberg, Henrik and Valdez-Jasso, Daniela and Sundnes, Joakim},
  journal       = {Frontiers in Physiology},
  volume        = {15},
  pages         = {1360389},
  year          = {2024},
  publisher     = {Frontiers Media SA}
}
@unpublished{poulain2022,
  title         = {{Multi-compartmental model of glymphatic clearance of solutes in brain tissue}},
  author        = {Poulain, Alexandre and Riseth, J{{\o}}rgen and Vinje, Vegard},
  url           = {https://hal.archives-ouvertes.fr/hal-03789563},
  note          = {working paper or preprint},
  year          = {2022},
  month         = sep,
  pdf           = {https://hal.archives-ouvertes.fr/hal-03789563/file/multicompartment-glymphatics.pdf},
  hal_id        = {hal-03789563},
  hal_version   = {v1}
}
@article{saetra2021,
  doi           = {10.1371/journal.pcbi.1008143},
  author        = {S\ae{}tra, Marte J. AND Einevoll, Gaute T. AND Halnes, Geir},
  journal       = {PLOS Computational Biology},
  publisher     = {Public Library of Science},
  title         = {An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain},
  year          = {2021},
  month         = {07},
  volume        = {17},
  pages         = {1--45},
  number        = {7}
}
@article{sætra2023,
  doi           = {10.1371/journal.pcbi.1010996},
  author        = {S\ae{}tra, Marte J. AND Ellingsrud, Ada J. AND Rognes, Marie E.},
  journal       = {PLOS Computational Biology},
  publisher     = {Public Library of Science},
  title         = {Neural activity induces strongly coupled electro-chemo-mechanical interactions and fluid flow in astrocyte networks and extracellular space--A computational study},
  year          = {2023},
  month         = {07},
  volume        = {19},
  url           = {https://doi.org/10.1371/journal.pcbi.1010996},
  pages         = {1--31},
  number        = {7}
}
@article{saetra2024,
  doi           = {10.1371/journal.pcbi.1012114},
  author        = {S\ae{}tra, Marte J. AND Mori, Yoichiro},
  journal       = {PLOS Computational Biology},
  publisher     = {Public Library of Science},
  title         = {An electrodiffusive network model with multicompartmental neurons and synaptic connections},
  year          = {2024},
  month         = {11},
  volume        = {20},
  url           = {https://doi.org/10.1371/journal.pcbi.1012114},
  pages         = {1--33},
  number        = {11}
}
@inbook{valnes2022,
  author        = {Mardal, Kent-Andr{\'e} and Rognes, Marie E. and Thompson, Travis B. and Valnes, Lars Magnus},
  title         = {{Getting started: from T1 images to simulation}},
  booktitle     = {{Mathematical Modeling of the Human Brain: From Magnetic Resonance Images to Finite Element Simulation}},
  year          = {2022},
  publisher     = {{Springer International Publishing}},
  address       = {Cham},
  pages         = {23--46},
  isbn          = {978-3-030-95136-8},
  doi           = {10.1007/978-3-030-95136-8_3}
}
@misc{zapf2023medical,
  title         = {Medical image registration using optimal control of a linear hyperbolic transport equation with a DG discretization},
  author        = {Bastian Zapf and Johannes Haubner and Lukas Baumg\"{a}rtner and Stephan Schmidt},
  year          = {2023},
  eprint        = {2305.03020},
  archiveprefix = {arXiv},
  primaryclass  = {math.NA}
}