The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals
Abstract
We report the topography of brain white matter hyperintensities (WMHs) on T2-weighted fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging in 477 healthy subjects aged 60-64 years selected randomly from the community. WMHs were delineated by using a computer algorithm. We found that all subjects had periventricular WMHs and 96.6% subjects also had deep WMHs. The mean volume of WMHs was 4.9 ml, comprising 0.83% of the white matter, of which 1.2 ml was severe in intensity. The deep WMHs were distributed throughout the cerebral hemispheres, with the occipital and frontal white matter bearing the greatest burden. The territory of the lenticulostriate arteries had the greatest WMHs. A white matter region of 4 mm adjacent to the cortex was not affected by hyperintensities. The mean (SD) number of discrete WMHs was 19.6 (7.1) per subject, of which 6.1 (4.4) were severe in intensity. Nearly half (48.6%) of the subjects had at least one large WMH (>12 mm diameter) and one eighth (12.5%) of the subjects had at least one large WMH that appeared to be severe in MRI. The overall load of WMHs was similar in men and women, but the latter had a higher proportion of their white matter so affected. This study provides the first detailed topographic analysis of WMHs in a large representative middle-aged sample, emphasizes their high prevalence in mid-adult life and raises issues about their etiology and significance.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“WMLs are highly prevalent in the brains of elderly and even middle-aged individuals,54,55 and the lesions have to be extensive to be clinically significant.”
From Diagnostic Criteria for Vascular Cognitive Disorders: A VASCOG Statement (2014) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging
Fazekas, F., Chawluk, J. B., Alavi, A. A., Hurtig, H. I., & Zimmerman, R. A. (1987) · American Journal of Roentgenology · doi:10.2214/ajr.149.2.351 · Cited by 5,422
Pathologic correlates of incidental MRI white matter signal hyperintensities
Fazekas, F., Kleinert, R., Offenbacher, H., Schmidt, R., Kleinert, G., Payer, F., Radner, H., & Lechner, H. (1993) · Neurology · doi:10.1212/wnl.43.9.1683 · Cited by 1,619
Toga, A. W., & Thompson, P. M. (2003) · Nature reviews. Neuroscience · doi:10.1038/nrn1009 · Cited by 1,498
Longstreth, W. T., Manolio, T. A., Arnold, A., Burke, G. L., Bryan, N., Jungreis, C. A., Enright, P. L., O'Leary, D., & Fried, L. (1996) · Stroke · doi:10.1161/01.str.27.8.1274 · Cited by 1,464
Pathogenesis of Leukoaraiosis: A Review
Pantoni, L., & Garcia, J. H. (1997) · Stroke · doi:10.1161/01.str.28.3.652 · Cited by 1,225
O'Brien, J. T., Erkinjuntti, T., Reisberg, ., Román, G. C., Pantoni, L., Bowler, J., Ballard, C., DeCarli, C., Gorelick, P., Rockwood, K., et al. (2014) · International Journal of Stroke · doi:10.1111/ijs.12374_18 · Cited by 1,165
Multimodal image coregistration and partitioning--a unified framework.
Ashburner, J., & Friston, K. (1997) · NeuroImage · doi:10.1006/nimg.1997.0290 · Cited by 1,051
Breteler, M. M. B., Swieten, J. C. V., Bots, M. L., Grobbee, D. E., Claus, J. J., Hout, J. H. W. V. D., van Harskamp, F., Tanghe, H. L. J., de Jong, P. T. V. M., van Gijn, J., et al. (1994) · Neurology · doi:10.1212/wnl.44.7.1246 · Cited by 1,006
Research works printed but not linked: 1
- Moody, 1990, Does the tortuosity in cerebral arterioles impair down-regulation in hypertensive and elderly normotensive? A hypothesis and computer model, Clin. Neurosurg., 37, 372
Cited by
Sorted by citations
Items 201–300 of 316
Quattrocchi, C. C., Errante, Y., Mallio, C. A., Carideo, L., Scarciolla, L., Santini, D., Tonini, G., & Zobel, B. B. (2014) · Journal of Neuro-Oncology · doi:10.1007/s11060-014-1554-7 · Cited by 10
Hernández, M. D. C. V., González‐Castro, V., Ghandour, D., Wang, X., Doubal, F., Maniega, S. M., Armitage, P. A., & Wardlaw, J. M. (2016) · Neuroradiology · doi:10.1007/s00234-016-1648-3 · Cited by 11
Igwe, K. C., Lao, P. J., Vorburger, R. S., Banerjee, A., Rivera, A. M., Chesebro, A. G., Laing, K. K., Manly, J. J., & Brickman, A. M. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.10.007 · Cited by 11
Reproducibility and repeatability of magnetic resonance imaging in dementia.
Morgan, C. A., Roberts, R., Chaffey, T., Tahara-Eckl, L., Meer, M. V. D. V. D., Günther, M., Anderson, T., Cutfield, N., Dalrymple-Alford, J., Kirk, I., et al. (2022) · Physica medica (Testo stampato) · doi:10.1016/j.ejmp.2022.06.012 · Cited by 11
Yamashita, Y., Arimura, H., & Tsuchiya, K. (2008) · Academic Radiology · doi:10.1016/j.acra.2008.03.004 · Cited by 11
Duan, D., Shen, L., Cui, C., Shu, T., & Zheng, J. (2017) · Lipids in Health and Disease · doi:10.1186/s12944-017-0436-3 · Cited by 10
Low, A., Ng, K. P., Chander, R. J., Wong, B. Y. X., & Kandiah, N. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190159 · Cited by 10
Bal, S., Goyal, M., Smith, E. E., & Demchuk, A. M. (2014) · Handbook of clinical neurology · doi:10.1016/b978-0-444-53480-4.00021-7 · Cited by 10
White Matter Hyperintensities on 1.5 and 3 Tesla Brain MRI in Healthy Individuals
Perri, C. D., Dwyer, M. G., Bergsland, N., Schirda, C., Poloni, G., Wack, D. S., Cox, J. L., Dalaker, T. O., Ranza, L., Zivadinov, R., et al. (2013) · Journal of Biomedical Graphics and Computing · doi:10.5430/jbgc.v3n3p53 · Cited by 10
Mild Cognitive Impairment and Normal Aging
Rog, L. A., & Fink, J. W. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3106-0_16 · Cited by 10
Frontal Lobe Atrophy in Depression after Stroke
Tang, W. K., Chen, Y. K., Lu, J. Y., Mok, V., Chu, C. W., Ungvari, G. S., & Wong, K. S. (2013) · Stroke Research and Treatment · doi:10.1155/2013/424769 · Cited by 9
Ollila, H., Pihlajamaa, J., Martola, J., Kuusela, L., Blennow, K., Zetterberg, H., Salmela, V. R., Hokkanen, L., Tiainen, M., & Hästbacka, J. (2023) · Journal of Critical Care · doi:10.1016/j.jcrc.2023.154502 · Cited by 10
A robust automated segmentation method for white matter hyperintensity of vascular-origin
He, H., Jiang, J. C., Peng, S., He, C., Sun, T., Fan, F., Song, H., Sun, D., Xu, Z., Wu, S., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121279 · Cited by 9
Neuroanatomical predictors of complex skill acquisition during video game training
Kovbasiuk, A., Lewandowska, P., Brzezicka, A., & Kowalczyk‐Grębska, N. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.834954 · Cited by 9
Bahrani, A. A., Al‐Janabi, O. M., Abner, E. L., Bardach, S. H., Kryscio, R. J., Wilcock, D. M., Smith, C. D., & Jicha, G. A. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108391 · Cited by 8
The knowledge domain of cognitive neuroscience of aging: A Scientometric and bibliometric analysis
Jiang, J., Fan, L., & Liu, J. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.999594 · Cited by 8
Vascular cognitive impairment: prodrome to VaD?
Mayda, A., & DeCarli, C. (2009) · Vascular Cognitive Impairment in Clinical Practice · doi:10.1017/cbo9780511575976.003 · Cited by 7
Caçoilo, A., Dortdivanlioglu, B., Rusinek, H., & Weickenmeier, J. (2023) · Brain Multiphysics · doi:10.1016/j.brain.2023.100072 · Cited by 7
Brain and retina in Alzheimer's disease: Pathological intersections and estimates from imaging
Banihashemi, M. A., Krance, S. H., Ji, P. X., Koo, M., Ottoy, J., Swartz, R. H., Kertes, P. J., Hudson, C., Goubran, M., & Black, S. E. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70884 · Cited by 7
Dong, C., Thalamuthu, A., Jiang, J., Mather, K. A., Brodaty, H., Sachdev, P. S., & Wen, W. X. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab323 · Cited by 6
Self-citationSun, Y., Xia, W., Wei, R., Dai, Z., Sun, X., Zhu, J., Song, B., & Wang, H. (2024) · Neurology and Therapy · doi:10.1007/s40120-024-00652-3 · Cited by 6
Distinct spatiotemporal patterns of white matter hyperintensity progression
Chung, J., Park, G., Ryu, W., Schellingerhout, D., Kim, H., Gwak, D., Haddad, E., Jahanshad, N., Kim, B. J., Hong, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64704-4 · Cited by 6
Vulnerability to memory decline in aging revealed by a mega-analysis of structural brain change
Dídac Vidal-piñeiro, A., Sørensen, Ø., Strømstad, M., Amlien, I. K., Baaré, W. F. C., Bartrés‐Faz, D., Brandmaier, A. M., Cattaneo, G., Düzel, S., Ghisletta, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66354-y · Cited by 14
Zheng, G., Fei, B., Ge, A., Liu, Y., Liu, Y., Yang, Z., Chen, Z., Wang, X., Wang, H., & Ding, J. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-847 · Cited by 5
Rudolph, M. D., Cohen, J. R., & Madden, D. J. (2024) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-024-01219-3 · Cited by 7
Computational Atlases of Severity of White Matter Lesions in Elderly Subjects with MRI
Hadjidemetriou, S., Lorenzen, P., Schuff, N. W., Mueller, S. G., & Weiner, M. W. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_54 · Cited by 5
Jongen, C., van der Grond, J., Anbeek, P., Viergever, M. A., Biessels, G. J., & Pluim, J. P. W. (2008) · Human Brain Mapping · doi:10.1002/hbm.20651 · Cited by 5
Quantitative susceptibility mapping (QSM) and R2* of silent cerebral infarcts in sickle cell anemia
Murdoch, R., Stotesbury, H., Kawadler, J. M., Saunders, D. E., Kirkham, F. J., & Shmueli, K. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.1000889 · Cited by 5
Li, K., Wang, S., Luo, X., Zeng, Q., Liu, X., Hong, L., Li, J., Hong, H., Xu, X., Zhang, Y., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29022 · Cited by 6
Kim, J. S., Lee, S., Kim, G. E., Oh, D. J., Moon, W., Bae, J. B., Han, J. W., Byun, S., Suh, S. W., Choi, Y. Y., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102607 · Cited by 5
An FMRI-Compatible Symbol Search Task
Liebel, S. W., Clark, U. S., Xu, X., Riskin-Jones, H. H., Hawkshead, B. E., Schwarz, N. F., Labbe, D. R., Jerskey, B. A., & Sweet, L. H. (2015) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715000144 · Cited by 5
Structural Brain Abnormalities in Bipolar Disorder
Matsuo, K., Sanches, M., Brambilla, P., & Soares, J. C. (2012) · Oxford University Press eBooks · doi:10.1093/med/9780199797608.003.0022 · Cited by 5
Mito, R., Dhollander, T., Xia, Y., Raffelt, D. A., Salvado, O., Churilov, L. P., Rowe, C. C., Brodtmann, A. G., Villemagne, V. L., & Connelly, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/623124 · Cited by 5
Chapter 4 Neuroimaging of Cognitive Impairments in Vascular Disease
Perri, C. D., Dalaker, T. O., Beyer, M. K., & Zivadinov, R. (2009) · International review of neurobiology · doi:10.1016/s0074-7742(09)00404-8 · Cited by 4
Park, D. C., & Farrell, M. E. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-411469-2.00005-4 · Cited by 4
Lee, J. W., Kim, D., Lee, S., Choi, S., Kong, S., Hwangbo, L., Lee, J. I., & Oh, S. (2021) · Annals of Otology Rhinology & Laryngology · doi:10.1177/00034894211018925 · Cited by 4
Jiang, J., Thalamuthu, A., Koch, F. C., Liu, T., Xu, Q., Trollor, J. N., Ames, D. J., Wright, M. J., Catts, V. S., Sachdev, P. S., et al. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00169 · Cited by 4
Self-citationSynapse: A Clinical Trial Examining the Impact of Actively Engaging the Aging Mind
Lodi‐Smith, J., & Park, D. C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-0636-6_4 · Cited by 4
Grau-Olivares, M., Arboix, A., David Bartrés-faz, A., & Junqué, C. (2008) · European Journal of Neurology · doi:10.1111/j.1468-1331.2008.02220.x · Cited by 4
17 White Matter Hyperintensities in Aging and Dementia
Mayda, A. B. V., Yoshita, M., & DeCarli, C. S. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195328875.003.0017 · Cited by 4
Heckner, M. K., Cieslik, E. C., Eickhoff, S. B., Camilleri, J. A., Hoffstaedter, F., & Langner, R. (2020) · doi:10.1101/2020.07.15.204941
Structural changes in the aging brain
Riddle, M., & Taylor, W. D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-800136-3.00005-3 · Cited by 4
Liu, D., Ma, X., Li, X., Li, K., Bu, Q., & Zhou, L. (2025) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2025.111958 · Cited by 3
Memory, brain and aging: The good, the bad and the promising
Ober, B. A. (2008) · California Agriculture · doi:10.3733/ca.v064n04p174 · Cited by 3
Rajput, R., Kaur, R., Chadha, R., Mani, S., Rachana, R., Kaur, H., & Singh, M. (2018) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-5225-5282-6.ch001 · Cited by 4
Alijanpourotaghsara, A., Strelnikov, D., Piroska, M., Szalontai, L., Forgó, B., Jokkel, Z., Persely, A., Hernyes, A., Kozák, L. R., Szabó, Á., et al. (2022) · Medicina · doi:10.3390/medicina58101425 · Cited by 3
van Velkinburgh, J. C., Herbst, M. D., & Casper, S. M. (2023) · World Journal of Clinical Cases · doi:10.12998/wjcc.v11.i19.4477 · Cited by 3
Psychiatric consequences of small vessel disease
Sachdev, P. S., & Reutens, S. G. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139382694.022 · Cited by 3
Self-citationThe Default Mode Network and Executive Function
Brown, C. (2017) · UKnowledge (University of Kentucky) · doi:10.13023/etd.2017.367 · Cited by 3
Mathewson, K. J., Beaton, E. A., Hobbs, D. A., Hall, G. B., Schulkin, J. A., Lieshout, R. J. V., Saigal, S., & Schmidt, L. A. (2023) · Clinical Neurophysiology · doi:10.1016/j.clinph.2023.06.006 · Cited by 2
Azenha, D., Arantes, M., Pereira-Macedo, J., Romana-Dias, L., Myrcha, P., Andrade, J. P., & Rocha-Neves, J. P. (2024) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2024.108354 · Cited by 2
Spinocerebellar ataxia masquerading as multiple sclerosis, a case report
Wheeler, D., Bezih, M., & Lannen, N. (2024) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2024.578385 · Cited by 2
White Matter Changes: New Perspectives on Imaging, Clinical Aspects, and Intervention
Madureira, S., Verdelho, A., Pantoni, L., & Scheltens, P. (2011) · Journal of Aging Research · doi:10.4061/2011/841913 · Cited by 2
Krukow, P. (2014) · Polish Psychological Bulletin · doi:10.2478/ppb-2014-0041 · Cited by 2
MRI Features and Site-specific Factors of Ischemic Changes in White Matter: A Retrospective Study
Zhang, Y., Liu, N., Liu, K., Pan, C., Cai, X., Yang, S.-Q., Tang, Z., & Xu, S. (2018) · Current Medical Science · doi:10.1007/s11596-018-1881-6 · Cited by 2
Lee, C. H., & Kim, D. H. (2020) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617720000107 · Cited by 2
Kara, F., Tosakulwong, N., Lesnick, T. G., Fought, A. J., Kendall‐Thomas, J., Kapoor, E., Faubion, L. L., Schwarz, C. G., Senjem, M. L., Fields, J. A., et al. (2024) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000002481 · Cited by 2
Segmentation of magnetic resonance images of brain and abdomen
Annamalai, S. (2010) · doi:10.32657/10356/47466 · Cited by 2
Jain, S., Solis‐Urra, P., Lesnovskaya, A., Wan, L., Molina‐Hidalgo, C., Collins, A., Huang, H., Sewell, K. R., Reed, R. G., Rogers, R. J., et al. (2025) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2025.106223 · Cited by 1
Ho, M. P., Song, Y., Sachdev, P. S., Fan, L., Jiang, J., & Wen, W. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1098 · Cited by 2
Self-citationDUNHAM, C. M., Huang, G. S., Ugokwe, K. T., Brocker, B. P., & Merrell, R. (2026) · Cureus · doi:10.7759/cureus.105775 · Cited by 1
Uduma, F., & Ongolo, C. (2013) · OMICS Journal of Radiology · doi:10.4172/2167-7964.1000136 · Cited by 1
Metabolic Syndrome is Associated with White Matter Hyperintensity in Stroke Patients
Zhang, Z., Zhang, W., Li, J., & Xiao, M. (2017) · Brain Impairment · doi:10.1017/brimp.2017.9 · Cited by 1
Rabbani, M., Shaygannejad, V., Bahrami, M., & Badiei, S. (2021) · Journal of Research in Medical Sciences · doi:10.4103/jrms.jrms_726_17 · Cited by 2
Association between Cerebral Small Vessel and Alzheimer’s Disease
Lee, K. H., & Kang, K. M. (2022) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2022.0041 · Cited by 1
Gwo, C., Zhu, D. C., & Zhang, R. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1028929 · Cited by 1
Rajagopalan, J., & Jacob, A. M. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-86712-5_57 · Cited by 1
Chancay, O., Haro, T., Yapur, M., Alvarado, R., Pastor, M. A., & Loayza, F. R. (2015) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 1
Haranas, I. I., Gkigkitzis, I., Kotsireas, I., & Austerlitz, C. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-57379-3_4 · Cited by 1
Wang, M.-L., Yu, M., Li, W., & Li, Y. (2021) · Current Alzheimer Research · doi:10.2174/1567205018666210324125116 · Cited by 1
The Overview of Cognitive Aging Models
Wang, D., Tang, Z., Zhao, J., & Lü, P. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-981-99-1627-6_4 · Cited by 1
Jokinen, H. (2007) · Työväentutkimus Vuosikirja · Cited by 1
Impacto de la enfermedad cardiovascular en el síndrome de Sjögren primario
Novo, M. P. D. L. (2010) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) · Cited by 1
Structural neuroimaging: defining the cerebral context for cognitive rehabilitation
Ramirez, J., Gao, F. Q., & Black, S. E. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9781316529898.011 · Cited by 2
Emotion Regulation in Relation to Cognitive Functioning in the Preclinical Stages of Dementia
Meltzer, E. P. (2016) · CUNY Academic Works (City University of New York) · Cited by 1
An Enhanced Conditional Variational Autoencoder-Based Normative Model for Neuroimaging AnalysisPreprint
Ho, M. P., Song, Y., Sachdev, P. S., Jiang, J., & Wen, W. X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.05.631276 · Cited by 1
Self-citationMultimodal MR Prediction Models for Late-Life Depression and Treatment Response
Patel, M. J. (2014) · D-Scholarship@Pitt (University of Pittsburgh)
Structural brain MRI and cognition in newly diagnosed Parkinson’s disease
Dalaker, T. O. (2010) · NORA - Norwegian Open Research Archives
Dalaker, T. O., Beyer, M. K., Stosic, M., & Zivadinov, R. (2009) · Springer eBooks · doi:10.1007/978-1-4020-9831-4_2
Onami, S. H. (2010) · Berkeley Scientific Journal · doi:10.5070/bs3132007631
MRI correlates of vascular cerebral lesions and cognitive impairment
van Straaten, E. C. W. (2008) · Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)
Neuropsychological and structural brain correlates of lacunar infarcts
Olivares, M. (2008) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Chen, Y. (2013) · Journal of Neurophysiology and Neurological Disorders · doi:10.17303/jnnd.2014.103
Kim, N., Hwang, S., Koo, J., Kwon, O.-H., Park, J., Lee, J.-J., & Kim, B. (2009)
The Validity of Vascular Depression as a Diagnostic Construct
Taylor, L., Mohan, A., & Sachdev, P. S. (2016) · Springer eBooks · doi:10.1007/978-3-319-32480-7_7
Self-citationSantiago, C. (2014) · TSpace (University of Toronto)
Raya, S. P. A. B., & José, J. (2016) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Tosto, G., Zimmerman, M. E., Hamilton, J. L., Carmichael, O. T., & Brickman, A. M. (2015)
The Effect of Lifelong Musicianship on Age-related Changes in Auditory Processing
Zendel, B. R. (2012) · TSpace (University of Toronto)
Quantitative Measurement of White Matter Lesions Using Semi-automated Method
Shieh, Y. Y., Chang, C.-H., & Goodwin, S. (2017) · International Journal of Bioscience Biochemistry and Bioinformatics · doi:10.17706/ijbbb.2017.7.4.238-244
Classification and grading of white matter hyperintensity severity by using automated detection
Isa, I. S., Sulaiman, S. N., Tahir, N. M., Abdullah, M. F., Soh, Z. H. C., Mustapha, M., & Karim, N. K. A. (2017) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2017.8284404
Echávarri, C., Cristina, M., Martínez, C., Isabel, A., Díaz, A., & Giadás, T. C. (2010)
Visser, V. L., Rusinek, H., & Weickenmeier, J. (2021) · Research Square · doi:10.21203/rs.3.rs-635671/v1
CHARACTERISTICS OF WHITE MATTER HYPOINTENSITY IN NORMAL COGNITIVE VIETNAMESE ADULTS USING MRI
Dong, T. Q., Quy, V. K., Vinh, L. G., Anh, T. H., & Chien, N. L. (2021) · Tạp chí Y học Việt Nam · doi:10.51298/vmj.v506i1-2.986
뇌혈관위험인자가 없는 젊은 두통 환자의 백색질 이상: 편두통과 긴장형두통
김나영, 구자성, Kwon, O., Park, J., Kim, N. K., Kun, K. B., & 황선태 (2009)
Rubinstein, S. M. (2005) · Stroke · doi:10.1161/01.str.0000185646.25205.06
Structural brain abnormalities in bipolar disorder
Bellani, M., Zovetti, N., Sanches, M., Matsuo, K., Soares, J. C., & Brambilla, P. (2022) · Oxford University Press eBooks · doi:10.1093/med/9780197574522.003.0002
Rajput, R., Kaur, R., Chadha, R., Mani, S., Rachana, R., Kaur, H., & Singh, M. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-5295-0.ch008
Moonis, M. (2005) · Stroke · doi:10.1161/01.str.0000185702.47579.7e
Metadata
- Authors
- Wen, W., & Sachdev, P.
- Affiliations
- UNSW Sydney (AU)
- Corresponding author
- Sachdev, Perminder
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2004-05
- Volume
- 22
- Issue
- 1
- Pages
- 144-154
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2070268750
- Cited by
- 315
- References
- 50
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 8 · 2023: 13 · 2022: 13 · 2021: 18 · 2020: 8 · 2019: 12 · 2018: 14 · 2017: 13 · 2016: 14 · 2015: 13 · 2014: 19 · 2013: 15 · 2012: 19
- FWCI
- 9.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of New South Wales
- National Health and Medical Research Council
- Topics
- Health Sciences › Medicine › Clinical Neurology › Cerebrovascular and genetic disorders
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- white matter hyperintensities, periventricular white matter, deep white matter hyperintensities, FLAIR MRI, lenticulostriate arteries, white matter volume, middle-aged adults, sex differences, prevalence, white matter disease
- MeSH terms
- Aging, Australia, Brain, Brain Mapping, Cerebral Arteries, Education, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Psychiatric Status Rating Scales, Reference Values, Sex Characteristics, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review