Kent‐André Mardal

7.7k total citations · 5 hit papers
103 papers, 4.7k citations indexed

About

Kent‐André Mardal is a scholar working on Cellular and Molecular Neuroscience, Computational Mechanics and Neurology. According to data from OpenAlex, Kent‐André Mardal has authored 103 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Cellular and Molecular Neuroscience, 31 papers in Computational Mechanics and 24 papers in Neurology. Recurrent topics in Kent‐André Mardal's work include Cerebrospinal fluid and hydrocephalus (43 papers), Advanced Numerical Methods in Computational Mathematics (27 papers) and Spinal Dysraphism and Malformations (23 papers). Kent‐André Mardal is often cited by papers focused on Cerebrospinal fluid and hydrocephalus (43 papers), Advanced Numerical Methods in Computational Mathematics (27 papers) and Spinal Dysraphism and Malformations (23 papers). Kent‐André Mardal collaborates with scholars based in Norway, United States and Sweden. Kent‐André Mardal's co-authors include Anders Logg, Garth N. Wells, Ragnar Winther, Geir Ringstad, Per Kristian Eide, Are Hugo Pripp, Vegard Vinje, Lars Magnus Valnes, Victor M. Haughton and Hans Petter Langtangen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.

In The Last Decade

Kent‐André Mardal

98 papers receiving 4.5k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book

2012 954 citations Kent‐André Mardal et al. profile →
Brain-wide glymphatic enhancement and clearance in humans assessed with MRI

2018 355 citations Geir Ringstad, Kyrre E. Emblem et al. profile →
Sleep deprivation impairs molecular clearance from the human brain

2020 224 citations Per Kristian Eide, Vegard Vinje et al. profile →
The glymphatic system: Current understanding and modeling

2022 193 citations Tomas Bohr, Poul G. Hjorth et al. iScience profile →
Sleep cycle-dependent vascular dynamics in male mice and the predicted effects on perivascular cerebrospinal fluid flow and solute transport

2023 82 citations Laura Bojarskaite, Alexandra Vallet et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Kent‐André Mardal Norway	34	1.6k	1.2k	1.2k	649	578	103	4.7k
Yiannis Ventikos United Kingdom	37	317 0.2×	1.3k 1.1×	912 0.8×	88 0.1×	108 0.2×	167	4.2k
Marie E. Rognes Norway	18	331 0.2×	876 0.7×	162 0.1×	359 0.6×	87 0.2×	60	2.6k
Dzung L. Pham United States	44	306 0.2×	248 0.2×	1.2k 1.0×	121 0.2×	128 0.2×	194	9.8k
Gilles Bertrand France	34	352 0.2×	224 0.2×	847 0.7×	296 0.5×	152 0.3×	204	4.0k
Eric A. Swanson United States	46	531 0.3×	191 0.2×	871 0.7×	84 0.1×	242 0.4×	147	20.9k
Akram Aldroubi United States	39	145 0.1×	1.7k 1.4×	235 0.2×	132 0.2×	99 0.2×	134	9.8k
David A. Peters United States	40	364 0.2×	1.6k 1.4×	61 0.1×	54 0.1×	179 0.3×	258	6.3k
Bart M. ter Haar Romeny Netherlands	39	275 0.2×	384 0.3×	416 0.4×	112 0.2×	61 0.1×	195	8.9k
Pierrick Coupé France	44	407 0.3×	392 0.3×	498 0.4×	32 0.0×	99 0.2×	139	7.7k
Benoît M. Dawant United States	56	838 0.5×	316 0.3×	1.3k 1.1×	36 0.1×	37 0.1×	379	10.6k

Countries citing papers authored by Kent‐André Mardal

Since Specialization

Citations

This map shows the geographic impact of Kent‐André Mardal's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kent‐André Mardal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kent‐André Mardal more than expected).

Fields of papers citing papers by Kent‐André Mardal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kent‐André Mardal. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kent‐André Mardal. The network helps show where Kent‐André Mardal may publish in the future.

Co-authorship network of co-authors of Kent‐André Mardal

This figure shows the co-authorship network connecting the top 25 collaborators of Kent‐André Mardal. A scholar is included among the top collaborators of Kent‐André Mardal based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kent‐André Mardal. Kent‐André Mardal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Koch, Timo & Kent‐André Mardal. (2025). Estimation of fluid flow velocities in cortical brain tissue driven by the microvasculature. Interface Focus. 15(1). 20240042–20240042.

Storås, Tryggve Holck, et al.. (2025). T2 ‐Weighted T1 Mapping and Automated Segmentation of CSF : Assessment of Solute Gradients in the Healthy Brain. Journal of Magnetic Resonance Imaging.

Badia, Santiago, et al.. (2024). Efficient and reliable divergence-conforming methods for an elasticity-poroelasticity interface problem. Computers & Mathematics with Applications. 157. 173–194. 1 indexed citations

Bojarskaite, Laura, Alexandra Vallet, Daniel M. Bjørnstad, et al.. (2023). Sleep cycle-dependent vascular dynamics in male mice and the predicted effects on perivascular cerebrospinal fluid flow and solute transport. Nature Communications. 14(1). 953–953. 82 indexed citations breakdown →

Vinje, Vegard, et al.. (2023). Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation. Fluids and Barriers of the CNS. 20(1). 62–62. 41 indexed citations

Bohr, Tomas, Poul G. Hjorth, Sebastian C. Holst, et al.. (2022). The glymphatic system: Current understanding and modeling. iScience. 25(9). 104987–104987. 193 indexed citations breakdown →

Mardal, Kent‐André, et al.. (2022). Simulating epileptic seizures using the bidomain model. Scientific Reports. 12(1). 10065–10065. 2 indexed citations

Young, Bruce A., et al.. (2021). Variations in the cerebrospinal fluid dynamics of the American alligator (Alligator mississippiensis). Fluids and Barriers of the CNS. 18(1). 11–11. 16 indexed citations

Vinje, Vegard, et al.. (2020). Intracranial pressure elevation alters CSF clearance pathways. Fluids and Barriers of the CNS. 17(1). 29–29. 50 indexed citations

10.

Buccino, Alessio Paolo, Miroslav Kuchta, Karoline Horgmo Jæger, et al.. (2019). How does the presence of neural probes affect extracellular potentials?. Journal of Neural Engineering. 16(2). 26030–26030. 19 indexed citations

11.

Lindstrøm, Erika Kristina, Geir Ringstad, Angelika Sorteberg, et al.. (2018). Magnitude and direction of aqueductal cerebrospinal fluid flow: large variations in patients with intracranial aneurysms with or without a previous subarachnoid hemorrhage. Acta Neurochirurgica. 161(2). 247–256. 11 indexed citations

12.

Mortensen, Mikael, Miroslav Kuchta, Soroush Heidari Pahlavian, et al.. (2017). A numerical investigation of intrathecal isobaric drug dispersion within the cervical subarachnoid space. PLoS ONE. 12(3). e0173680–e0173680. 21 indexed citations

13.

Pozo, José M., et al.. (2017). Robustness of common hemodynamic indicators with respect to numerical resolution in 38 middle cerebral artery aneurysms. PLoS ONE. 12(6). e0177566–e0177566. 11 indexed citations

14.

Langtangen, Hans Petter, et al.. (2016). Computational Investigation of Cerebrospinal Fluid Dynamics in the Posterior Cranial Fossa and Cervical Subarachnoid Space in Patients with Chiari I Malformation. PLoS ONE. 11(10). e0162938–e0162938. 11 indexed citations

15.

Linge, Svein, Kent‐André Mardal, Victor M. Haughton, & Anders Helgeland. (2012). Simulating CSF Flow Dynamics in the Normal and the Chiari I Subarachnoid Space during Rest and Exertion. American Journal of Neuroradiology. 34(1). 41–45. 14 indexed citations

16.

Haughton, Victor M., et al.. (2012). Patient-Specific 3D Simulation of Cyclic CSF Flow at the Craniocervical Region. American Journal of Neuroradiology. 33(9). 1756–1762. 33 indexed citations

17.

Jiang, Jingfeng, Kevin M. Johnson, Kristian Valen‐Sendstad, et al.. (2011). Flow characteristics in a canine aneurysm model: A comparison of 4D accelerated phase‐contrast MR measurements and computational fluid dynamics simulations. Medical Physics. 38(11). 6300–6312. 34 indexed citations

18.

Mardal, Kent‐André, et al.. (2010). Characterization of Cyclic CSF Flow in the Foramen Magnum and Upper Cervical Spinal Canal with MR Flow Imaging and Computational Fluid Dynamics. American Journal of Neuroradiology. 31(6). 997–1002. 34 indexed citations

19.

Nielsen, Bjørn Fredrik, et al.. (2006). On the use of the bidomain equations for computing the transmembrane potential throughout the heart wall: An inverse problem. Computing in Cardiology Conference. 797–800. 3 indexed citations

20.

Sundnes, Joakim, Bjørn Fredrik Nielsen, Kent‐André Mardal, et al.. (2006). On the Computational Complexity of the Bidomain and the Monodomain Models of Electrophysiology. Annals of Biomedical Engineering. 34(7). 1088–1097. 79 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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