BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Høgestøl EA, Kaufmann T, Nygaard GO, Beyer MK, Sowa P, Nordvik JE, Kolskår K, Richard G, Andreassen OA, Harbo HF, Westlye LT. Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis. Front Neurol 2019;10:450. [PMID: 31114541 DOI: 10.3389/fneur.2019.00450] [Cited by in Crossref: 41] [Cited by in F6Publishing: 42] [Article Influence: 13.7] [Reference Citation Analysis]
Number Citing Articles
1 Condado JG, Cortes JM, Alzheimer’s Disease Neuroimaging Initiative. NeuropsychBrainAge: a biomarker for conversion from mild cognitive impairment to Alzheimer’s disease.. [DOI: 10.1101/2022.11.29.22282870] [Reference Citation Analysis]
2 Jirsaraie RJ, Kaufmann T, Bashyam V, Erus G, Luby JL, Westlye LT, Davatzikos C, Barch DM, Sotiras A. Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias. Human Brain Mapping 2022. [DOI: 10.1002/hbm.26144] [Reference Citation Analysis]
3 Han J, Kim SY, Lee J, Lee WH. Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data. Sensors (Basel) 2022;22:8077. [PMID: 36298428 DOI: 10.3390/s22208077] [Reference Citation Analysis]
4 Aamodt EB, Alnæs D, de Lange AG, Aam S, Schellhorn T, Saltvedt I, Beyer MK, Westlye LT. Longitudinal brain age prediction and cognitive function after stroke. Neurobiology of Aging 2022. [DOI: 10.1016/j.neurobiolaging.2022.10.007] [Reference Citation Analysis]
5 Khodanovich MY, Kamaeva DA, Naumova AV. Role of Demyelination in the Persistence of Neurological and Mental Impairments after COVID-19. IJMS 2022;23:11291. [DOI: 10.3390/ijms231911291] [Reference Citation Analysis]
6 Kular L, Klose D, Urdánoz-casado A, Ewing E, Planell N, Gomez-cabrero D, Needhamsen M, Jagodic M. Epigenetic clock indicates accelerated aging in glial cells of progressive multiple sclerosis patients. Front Aging Neurosci 2022;14:926468. [DOI: 10.3389/fnagi.2022.926468] [Reference Citation Analysis]
7 Reeves JA, Bergsland N, Dwyer MG, Wilding GE, Jakimovski D, Salman F, Sule B, Meineke N, Weinstock-Guttman B, Zivadinov R, Schweser F. Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis. Neuroimage 2022;:119503. [PMID: 35878723 DOI: 10.1016/j.neuroimage.2022.119503] [Reference Citation Analysis]
8 Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia (Engl Ed) 2022:S2173-5808(22)00075-X. [PMID: 35843587 DOI: 10.1016/j.nrleng.2020.10.013] [Reference Citation Analysis]
9 Chien C, Seiler M, Eitel F, Schmitz-hübsch T, Paul F, Ritter K. Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity. Multiple Sclerosis Journal - Experimental, Translational and Clinical 2022;8:205521732211097. [DOI: 10.1177/20552173221109770] [Reference Citation Analysis]
10 Holm MC, Leonardsen EH, Beck D, Dahl A, Kjelkenes R, de Lange AG, Westlye LT. Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort.. [DOI: 10.1101/2022.05.16.22275146] [Reference Citation Analysis]
11 Aamodt EB, Alnæs D, de Lange AG, Aam S, Schellhorn T, Saltvedt I, Beyer MK, Westlye LT. Longitudinal brain age prediction and cognitive function after stroke.. [DOI: 10.1101/2022.03.18.22272556] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 de Lange AG, Anatürk M, Rokicki J, Han LKM, Franke K, Alnaes D, Ebmeier KP, Draganski B, Kaufmann T, Westlye LT, Hahn T, Cole JH. Mind the gap: Performance metric evaluation in brain-age prediction. Hum Brain Mapp 2022. [PMID: 35312210 DOI: 10.1002/hbm.25837] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 11.0] [Reference Citation Analysis]
13 Beck D, de Lange AG, Alnæs D, Maximov II, Pedersen ML, Leinhard OD, Linge J, Simon R, Richard G, Ulrichsen KM, Dørum ES, Kolskår KK, Sanders AM, Winterton A, Gurholt TP, Kaufmann T, Steen NE, Nordvik JE, Andreassen OA, Westlye LT. Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults. Neuroimage Clin 2022;33:102949. [PMID: 35114636 DOI: 10.1016/j.nicl.2022.102949] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
14 Jacobs SAH, Muraro PA, Cencioni MT, Knowles S, Cole JH, Nicholas R. Worse Physical Disability Is Associated With the Expression of PD-1 on Inflammatory T-Cells in Multiple Sclerosis Patients With Older Appearing Brains. Front Neurol 2021;12:801097. [PMID: 35069428 DOI: 10.3389/fneur.2021.801097] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Leonardsen EH, Peng H, Kaufmann T, Agartz I, Andreassen OA, Celius EG, Espeseth T, Harbo HF, Høgestøl EA, de Lange A, Marquand AF, Vidal-piñeiro D, Roe JM, Selbæk G, Sørensen Ø, Smith SM, Westlye LT, Wolfers T, Wang Y. Deep neural networks learn general and clinically relevant representations of the ageing brain.. [DOI: 10.1101/2021.10.29.21265645] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
16 Beck D, de Lange AG, Pedersen ML, Alnaes D, Maximov II, Voldsbekk I, Richard G, Sanders AM, Ulrichsen KM, Dørum ES, Kolskår KK, Høgestøl EA, Steen NE, Djurovic S, Andreassen OA, Nordvik JE, Kaufmann T, Westlye LT. Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Hum Brain Mapp 2021. [PMID: 34626047 DOI: 10.1002/hbm.25680] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
17 Denissen S, Engemann D, De Cock A, Costers L, Baijot J, Laton J, Penner I, Grothe M, Kirsch M, D’hooghe M, D’haeseleer M, Dive D, De Mey J, Van Schependom J, Sima D, Nagels G. Brain age as a surrogate marker for information processing speed in multiple sclerosis.. [DOI: 10.1101/2021.09.03.21262954] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Wrigglesworth J, Ward P, Harding IH, Nilaweera D, Wu Z, Woods RL, Ryan J. Factors associated with brain ageing - a systematic review. BMC Neurol 2021;21:312. [PMID: 34384369 DOI: 10.1186/s12883-021-02331-4] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 9.0] [Reference Citation Analysis]
19 Shoeibi A, Khodatars M, Jafari M, Moridian P, Rezaei M, Alizadehsani R, Khozeimeh F, Gorriz JM, Heras J, Panahiazar M, Nahavandi S, Acharya UR. Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Comput Biol Med 2021;136:104697. [PMID: 34358994 DOI: 10.1016/j.compbiomed.2021.104697] [Cited by in Crossref: 45] [Cited by in F6Publishing: 49] [Article Influence: 45.0] [Reference Citation Analysis]
20 Høgestøl EA, Ghezzo S, Nygaard GO, Espeseth T, Sowa P, Beyer MK, Harbo HF, Westlye LT, Hulst HE, Alnæs D. Functional connectivity in multiple sclerosis modelled as connectome stability: A 5-year follow-up study. Mult Scler 2021;:13524585211030212. [PMID: 34259578 DOI: 10.1177/13524585211030212] [Reference Citation Analysis]
21 Luna A, Bernanke J, Kim K, Aw N, Dworkin JD, Cha J, Posner J. Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth. Hum Brain Mapp 2021;42:4568-79. [PMID: 34240783 DOI: 10.1002/hbm.25565] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
22 Hartmann M, Fenton N, Dobson R. Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med 2021;132:104337. [PMID: 33773193 DOI: 10.1016/j.compbiomed.2021.104337] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
23 Dunås T, Wåhlin A, Nyberg L, Boraxbekk CJ. Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance. Cereb Cortex 2021;31:3393-407. [PMID: 33690853 DOI: 10.1093/cercor/bhab019] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
24 Beck D, de Lange AG, Pedersen ML, Alnæs D, Maximov II, Voldsbekk I, Richard G, Sanders A, Ulrichsen KM, Dørum ES, Kolskår KK, Høgestøl EA, Steen NE, Djurovic S, Andreassen OA, Nordvik JE, Kaufmann T, Westlye LT. Cardiometabolic risk factors associated with brain age and accelerate brain ageing.. [DOI: 10.1101/2021.02.25.21252272] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
25 Jakimovski D, Eckert SP, Zivadinov R, Weinstock-Guttman B. Considering patient age when treating multiple sclerosis across the adult lifespan. Expert Rev Neurother 2021;21:353-64. [PMID: 33595379 DOI: 10.1080/14737175.2021.1886082] [Reference Citation Analysis]
26 Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia (Engl Ed) 2021:S0213-4853(20)30431-X. [PMID: 33549371 DOI: 10.1016/j.nrl.2020.10.017] [Reference Citation Analysis]
27 Brune S, Høgestøl EA, Cengija V, Berg-Hansen P, Sowa P, Nygaard GO, Harbo HF, Beyer MK. LesionQuant for Assessment of MRI in Multiple Sclerosis-A Promising Supplement to the Visual Scan Inspection. Front Neurol 2020;11:546744. [PMID: 33362682 DOI: 10.3389/fneur.2020.546744] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
28 Kolbeinsson A, Filippi S, Panagakis Y, Matthews PM, Elliott P, Dehghan A, Tzoulaki I. Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders. Sci Rep 2020;10:19940. [PMID: 33203906 DOI: 10.1038/s41598-020-76518-z] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 6.5] [Reference Citation Analysis]
29 de Lange AG, Anatürk M, Suri S, Kaufmann T, Cole JH, Griffanti L, Zsoldos E, Jensen DEA, Filippini N, Singh-Manoux A, Kivimäki M, Westlye LT, Ebmeier KP. Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. Neuroimage 2020;222:117292. [PMID: 32835819 DOI: 10.1016/j.neuroimage.2020.117292] [Cited by in Crossref: 42] [Cited by in F6Publishing: 46] [Article Influence: 21.0] [Reference Citation Analysis]
30 Wilkins JM, Gakh O, Kabiraj P, McCarthy CB, Tobin WO, Howe CL, Lucchinetti CF. Signatures of cell stress and altered bioenergetics in skin fibroblasts from patients with multiple sclerosis. Aging (Albany NY) 2020;12:15134-56. [PMID: 32640422 DOI: 10.18632/aging.103612] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
31 Papadopoulos D, Magliozzi R, Mitsikostas DD, Gorgoulis VG, Nicholas RS. Aging, Cellular Senescence, and Progressive Multiple Sclerosis. Front Cell Neurosci 2020;14:178. [PMID: 32694983 DOI: 10.3389/fncel.2020.00178] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 11.5] [Reference Citation Analysis]
32 Høgestøl EA, Ghezzo S, Nygaard GO, Espeseth T, Sowa P, Beyer MK, Harbo HF, Westlye LT, Hulst HE, Alnæs D. Five years functional connectivity reorganization without clinical or cognitive decline in MS.. [DOI: 10.1101/2020.06.19.20135558] [Reference Citation Analysis]
33 Barth C, de Lange AG. Towards an understanding of women's brain aging: the immunology of pregnancy and menopause. Front Neuroendocrinol 2020;58:100850. [PMID: 32504632 DOI: 10.1016/j.yfrne.2020.100850] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 7.5] [Reference Citation Analysis]
34 Cole JH, Raffel J, Friede T, Eshaghi A, Brownlee WJ, Chard D, De Stefano N, Enzinger C, Pirpamer L, Filippi M, Gasperini C, Rocca MA, Rovira A, Ruggieri S, Sastre-Garriga J, Stromillo ML, Uitdehaag BMJ, Vrenken H, Barkhof F, Nicholas R, Ciccarelli O; MAGNIMS study group. Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm. Ann Neurol 2020;88:93-105. [PMID: 32285956 DOI: 10.1002/ana.25746] [Cited by in Crossref: 33] [Cited by in F6Publishing: 35] [Article Influence: 16.5] [Reference Citation Analysis]
35 Kular L, Jagodic M. Epigenetic insights into multiple sclerosis disease progression. J Intern Med 2020;288:82-102. [DOI: 10.1111/joim.13045] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
36 Brune S, Høgestøl EA, Cengija V, Berg-hansen P, Sowa P, Nygaard GO, Harbo HF, Beyer MK. LesionQuant for assessment of MRI in multiple sclerosis - a promising supplement to the visual scan inspection.. [DOI: 10.1101/2020.04.01.20048249] [Reference Citation Analysis]
37 de Lange AG, Anatürk M, Kaufmann T, Cole JH, Griffanti L, Zsoldos E, Jensen D, Suri S, Filippini N, Singh-manoux A, Kivimäki M, Westlye LT, Ebmeier KP. Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study.. [DOI: 10.1101/2020.01.28.923094] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
38 Basher A, Kim BC, Lee KH, Jung HY. Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network. IEEE Access 2020;8:91725-39. [DOI: 10.1109/access.2020.2994388] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
39 Richard G, Kolskår K, Ulrichsen KM, Kaufmann T, Alnæs D, Sanders AM, Dørum ES, Monereo Sánchez J, Petersen A, Ihle-Hansen H, Nordvik JE, Westlye LT. Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training. Neuroimage Clin 2020;25:102159. [PMID: 31927499 DOI: 10.1016/j.nicl.2019.102159] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 7.7] [Reference Citation Analysis]
40 Zhavoronkov A, Li R, Ma C, Mamoshina P. Deep biomarkers of aging and longevity: from research to applications. Aging (Albany NY) 2019;11:10771-80. [PMID: 31767810 DOI: 10.18632/aging.102475] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 5.0] [Reference Citation Analysis]
41 Richard G, Kolskår K, Ulrichsen KM, Kaufmann T, Alnæs D, Sanders A, Dørum ES, Sánchez JM, Petersen A, Ihle-hansen H, Nordvik JE, Westlye LT. Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training.. [DOI: 10.1101/687079] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
42 [DOI: 10.1101/651521] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
43 [DOI: 10.1101/2021.04.08.21255106] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
44 [DOI: 10.1101/2021.05.16.444349] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Reference Citation Analysis]