Einar August Høgestøl
- Group leader; Associate Professor; MD, PhD
- +47 411 08 981
Einar August Høgestøl has a medical degree from University of Oslo in 2012. He then spent 1,5 year at the neurosurgical department at Oslo University Hospital. He defended his PhD project titled “MRI- and other biomarkers in early multiple sclerosis” in October 27th 2020. He also works as a Doctor in training at the Department of Neurology at Oslo University Hospital and regularly sees MS patients. He continues his research in the following areas of interest:
- Advanced MRI imaging markers in MS
- Biomarkers and MS
- Personalized treatment for MS patients
He is also an Associate Professor at the Psychological Department, University of Oslo, in collaboration with NORMENT, where he has many ongoing projects in collaboration with the Multimodal Imaging group lead by Professor Lars T. Westlye. Link to home page at UiO.
As for international collaboration he is involved in projects within the MAGNIMS consortium, in addition to specific collaborations with Karolinska Hospital in Sweden and Leiden Universiteit in Amsterdam.
He is main supervisor for MD. and PhD student Gisle Berg Helland, on a project investigating advanced imaging in working adults suffering a cerebral ischemic stroke (BRAKE-project). His PhD is titled: “Brain age after stroke in working-age adults”
He is co-supervisor for MD. and PhD student Lars Skattebøl, who is working with the ongoing NOR-MS clinical trail. His PhD is titled: “Advanced MRI analyses in the prospective randomized open-label blinded endpoint multicenter non-inferiority Norwegian study of Oral Cladribine and Rituximab in Multiple Sclerosis (NOR-MS)”.
Publications 2024
Prognostic value of single-subject grey matter networks in early multiple sclerosis
Brain, 147 (1), 135-146
DOI 10.1093/brain/awad288, PubMed 37642541
Information from ecological momentary assessments lead to over-medicalization: Yes
Mult Scler, 30 (8), 968-969
DOI 10.1177/13524585241253077, PubMed 38751213
Multiscale networks in multiple sclerosis
PLoS Comput Biol, 20 (2), e1010980
DOI 10.1371/journal.pcbi.1010980, PubMed 38329927
Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap
Neurology, 103 (10), e209976
DOI 10.1212/WNL.0000000000209976, PubMed 39496109
Publications 2023
Predicting disease severity in multiple sclerosis using multimodal data and machine learning
J Neurol, 271 (3), 1133-1149
DOI 10.1007/s00415-023-12132-z, PubMed 38133801
Immune cell subpopulations and serum neurofilament light chain are associated with increased risk of disease worsening in multiple sclerosis
J Neuroimmunol, 382, 578175
DOI 10.1016/j.jneuroim.2023.578175, PubMed 37573634
Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study
Cereb Cortex, 33 (12), 7322-7334
DOI 10.1093/cercor/bhad041, PubMed 36813475
T cell responses to SARS-CoV-2 vaccination differ by disease-modifying therapy for multiple sclerosis
JCI Insight, 8 (12)
DOI 10.1172/jci.insight.165111, PubMed 37159281
Publications 2022
Serum neurofilament light chain concentration predicts disease worsening in multiple sclerosis
Mult Scler, 28 (12), 1859-1870
DOI 10.1177/13524585221097296, PubMed 35658739
Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls
Clin Proteomics, 19 (1), 23
DOI 10.1186/s12014-022-09361-1, PubMed 35790914
Exploring Retinal Blood Vessel Diameters as Biomarkers in Multiple Sclerosis
J Clin Med, 11 (11)
DOI 10.3390/jcm11113109, PubMed 35683496
Deep neural networks learn general and clinically relevant representations of the ageing brain
Neuroimage, 256, 119210
DOI 10.1016/j.neuroimage.2022.119210, PubMed 35462035
Risk of fingolimod rebound after switching to cladribine or rituximab in multiple sclerosis
Mult Scler Relat Disord, 62, 103812
DOI 10.1016/j.msard.2022.103812, PubMed 35462167
Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study
Neuroimage Clin, 35, 103099
DOI 10.1016/j.nicl.2022.103099, PubMed 35772194
Publications 2021
Cardiometabolic risk factors associated with brain age and accelerate brain ageing
Hum Brain Mapp, 43 (2), 700-720
DOI 10.1002/hbm.25680, PubMed 34626047
Functional connectivity in multiple sclerosis modelled as connectome stability: A 5-year follow-up study
Mult Scler, 28 (4), 532-540
DOI 10.1177/13524585211030212, PubMed 34259578
Blood neurofilament light concentration at admittance: a potential prognostic marker in COVID-19
J Neurol, 268 (10), 3574-3583
DOI 10.1007/s00415-021-10517-6, PubMed 33743046
Publications 2020
LesionQuant for Assessment of MRI in Multiple Sclerosis-A Promising Supplement to the Visual Scan Inspection
Front Neurol, 11, 546744
DOI 10.3389/fneur.2020.546744, PubMed 33362682
Impact of treatment on cellular immunophenotype in MS: A cross-sectional study
Neurol Neuroimmunol Neuroinflamm, 7 (3)
DOI 10.1212/NXI.0000000000000693, PubMed 32139439
The genetic architecture of human brainstem structures and their involvement in common brain disorders
Nat Commun, 11 (1), 4016
DOI 10.1038/s41467-020-17376-1, PubMed 32782260
Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain
Nat Neurosci, 23 (2), 295
DOI 10.1038/s41593-019-0553-6, PubMed 31848485
Publications 2019
Quantitative proteomic analyses of CD4+ and CD8+ T cells reveal differentially expressed proteins in multiple sclerosis patients and healthy controls
Clin Proteomics, 16, 19
DOI 10.1186/s12014-019-9241-5, PubMed 31080378
Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis
Front Neurol, 10, 450
DOI 10.3389/fneur.2019.00450, PubMed 31114541
Symptoms of fatigue and depression is reflected in altered default mode network connectivity in multiple sclerosis
PLoS One, 14 (4), e0210375
DOI 10.1371/journal.pone.0210375, PubMed 30933977
Common brain disorders are associated with heritable patterns of apparent aging of the brain
Nat Neurosci, 22 (10), 1617-1623
DOI 10.1038/s41593-019-0471-7, PubMed 31551603
Publications 2018
SVM-based Tool to Detect Patients with Multiple Sclerosis Using a Commercial EMG Sensor
PR IEEE SEN ARRAY, 376-379
Supplementary motor area syndrome after surgery for parasagittal meningiomas
Acta Neurochir (Wien), 160 (3), 583-587
DOI 10.1007/s00701-018-3474-3, PubMed 29362933