Regional associations of white matter hyperintensities and early cortical amyloid pathology
Open accessAbstract
Abstract White matter hyperintensities (WMHs) have a heterogeneous aetiology, associated with both vascular risk factors and amyloidosis due to Alzheimer’s disease. While spatial distribution of both amyloid and WM lesions carry important information for the underlying pathogenic mechanisms, the regional relationship between these two pathologies and their joint contribution to early cognitive deterioration remains largely unexplored. We included 662 non-demented participants from three Amyloid Imaging to Prevent Alzheimer’s disease (AMYPAD)-affiliated cohorts: EPAD-LCS (N = 176), ALFA+ (N = 310), and EMIF-AD PreclinAD Twin60++ (N = 176). Using PET imaging, cortical amyloid burden was assessed regionally within early accumulating regions (medial orbitofrontal, precuneus, and cuneus) and globally, using the Centiloid method. Regional WMH volume was computed using Bayesian Model Selection. Global associations between WMH, amyloid, and cardiovascular risk scores (Framingham and CAIDE) were assessed using linear models. Partial least square (PLS) regression was used to identify regional associations. Models were adjusted for age, sex, and APOE-e4 status. Individual PLS scores were then related to cognitive performance in 4 domains (attention, memory, executive functioning, and language). While no significant global association was found, the PLS model yielded two components of interest. In the first PLS component, a fronto-parietal WMH pattern was associated with medial orbitofrontal–precuneal amyloid, vascular risk, and age. Component 2 showed a posterior WMH pattern associated with precuneus-cuneus amyloid, less related to age or vascular risk. Component 1 was associated with lower performance in all cognitive domains, while component 2 only with worse memory. In a large pre-dementia population, we observed two distinct patterns of regional associations between WMH and amyloid burden, and demonstrated their joint influence on cognitive processes. These two components could reflect the existence of vascular-dependent and -independent manifestations of WMH-amyloid regional association that might be related to distinct primary pathophysiology.
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
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?
The relationship between WMHs and Aβ is strongly determined by the spatial distribution of the two pathologies.
“evidence from recent studies has suggested that the relationship between WMHs and Aβ is strongly determined by the spatial distribution of the two pathologies [24]”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Development and validation of a geriatric depression screening scale: a preliminary report
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982) · Journal of Psychiatric Research · doi:10.1016/0022-3956(82)90033-4 · Cited by 15,025 · see citing works
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864 · see citing works
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359 · see citing works
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882 · see citing works
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491 · see citing works
Overdependence on degraded gist memory in Alzheimer's disease.
Gallo, D. A., Shahid, K. R., Olson, M. A., Solomon, T. M., Schacter, D. L., & Budson, A. E. (2006) · Neuropsychology · doi:10.1037/0894-4105.20.6.625 · Cited by 4,756 · see citing works
Moms, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., Mellits, E. D., & Clark, C. (1989) · Neurology · doi:10.1212/wnl.39.9.1159 · Cited by 4,130 · see citing works
Cognitive Stimulation in Patients with Dementia: Randomized Controlled Trial
Mapelli, D., Rosa, E. D., Nocita, R., & Sava, D. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353457 · Cited by 3,468 · see citing works
Villemagne, V. L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K. A., Salvado, O., Szoeke, C., Macaulay, S. L., Martins, R., Maruff, P., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70044-9 · Cited by 2,279 · see citing works
A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., Hampel, H., Jagust, W. J., Johnson, K. A., Knopman, D. S., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000002923 · Cited by 1,864 · see citing works
The Impact of Age on Cognition
Murman, D. (2015) · Seminars in Hearing · doi:10.1055/s-0035-1555115 · Cited by 1,619 · see citing works
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 · see citing works
The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET
Klunk, W. E., Koeppe, R. A., Price, J. C., Benzinger, T. L., Devous, M. D., Jagust, W. J., Johnson, K. A., Mathis, C. A., Minhas, D., Pontecorvo, M. J., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.07.003 · Cited by 1,348 · see citing works
(2001) · The Lancet · doi:10.1016/s0140-6736(00)03589-3 · Cited by 1,338 · see citing works
White matter hyperintensities, cognitive impairment and dementia: an update
Prins, N. D., & Scheltens, P. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.10 · Cited by 1,284 · see citing works
Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pérez, J. M., Evans, A. C., Initiative, T. A. D. N., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., et al. (2016) · Nature Communications · doi:10.1038/ncomms11934 · Cited by 1,202 · see citing works
Kivipelto, M., Ngandu, T., Laatikainen, T. K., Winblad, B. G., Soininen, H. S., & Tuomilehto, J. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70537-3 · Cited by 1,127 · see citing works
Mandelkow, E., & Mandelkow, E. (1998) · Trends in Cell Biology · doi:10.1016/s0962-8924(98)01368-3 · Cited by 951 · see citing works
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
Boulesteix, A., & Strimmer, K. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbl016 · Cited by 806 · see citing works
β-Amyloid-mediated vasoactivity and vascular endothelial damage
Thomas, T., Thomas, G., McLendon, C., Sutton, T., & Mullan, M. (1996) · Nature · doi:10.1038/380168a0 · Cited by 703 · see citing works
Neurovascular dysfunction and neurodegeneration in dementia and Alzheimer's disease
Nelson, A. R., Sweeney, M. D., Sagare, A. P., & Zlokovic, B. V. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2015.12.016 · Cited by 559 · see citing works
Utility of TICS‐M for the assessment of cognitive function in older adults
de Jager, C. A., Budge, M. M., & Clarke, R. (2003) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.830 · Cited by 455 · see citing works
Cardoso, M. J., Marc Modat, A., Wolz, R., Melbourne, A., Cash, D. M., Rueckert, D., & Sebastien Ourselin, A. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2418298 · Cited by 343 · see citing works
Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly
Vemuri, P., Lesnick, T. G., Przybelski, S. A., Knopman, D. S., Preboske, G. M., Kantarci, K., Raman, M. R., Machulda, M. M., Mielke, M. M., Lowe, V. J., et al. (2015) · Brain · doi:10.1093/brain/awu393 · Cited by 299 · see citing works
McAleese, K. E., Walker, L., Graham, S., Moya, E. L. J., Johnson, M. A., Erskine, D., Colloby, S. J., Dey, M., Martin-Ruiz, C. M., Taylor, J., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1738-2 · Cited by 271 · see citing works
Brickman, A. M., Provenzano, F. A., Muraskin, J., Manly, J. J., Blum, S., Apa, Z. L., Stern, Y., Brown, T. R., Luchsinger, J. A., & Mayeux, R. P. (2012) · Archives of Neurology · doi:10.1001/archneurol.2012.1527 · Cited by 252 · see citing works
LEAP: Learning embeddings for atlas propagation
Wolz, R., Aljabar, P., Hajnal, J. V., Hammers, A., & Rueckert, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.069 · Cited by 234 · see citing works
Huang, J., Friedland, R. P., & Auchus, A. P. (2007) · American Journal of Neuroradiology · doi:10.3174/ajnr.a0700 · Cited by 220 · see citing works
Effect of amyloid on memory and non-memory decline from preclinical to clinical Alzheimer’s disease
Lim, Y. Y., Maruff, P. T., Pietrzak, R. H., Ames, D. J., Ellis, K. A., Harrington, K., Lautenschlager, N. T., Szoeke, C. E. I., Martins, R. N., Masters, C. L., et al. (2013) · Brain · doi:10.1093/brain/awt286 · Cited by 207 · see citing works
Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers
for the Alzheimer’s Disease Neuroimaging Initiative, F. T. A. S., Salvadó, G., Molinuevo, J. L., Brugulat-Serrat, A., Falcon, C., Grau-Rivera, O., Suárez-Calvet, M., Pavia, J., Niñerola-Baizán, A., Perissinotti, A., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0478-z · Cited by 195 · see citing works
Self-citationInterpretation of latent-variable regression models
Kvalheim, O. M., & Karstang, T. V. (1989) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(89)80110-8 · Cited by 184 · see citing works
White Matter Changes in Dementia: Role of Impaired Drainage of Interstitial Fluid
Weller, R. O., Hawkes, C. A., Kalaria, R. N., Werring, D. J., & Carare, R. O. (2014) · Brain Pathology · doi:10.1111/bpa.12218 · Cited by 183 · see citing works
Molinuevo, J. L., Gramunt, N., Gispert, J. D., Fauria, K., Esteller, M., Minguillón, C., Sánchez‐Benavides, G., Huesa, G., Morán, S., Dal‐Ré, R., et al. (2016) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2016.02.003 · Cited by 177 · see citing works
Self-citationLampe, L., Masouleh, S. K., Kynast, J., Arélin, K., Steele, C. J., Löffler, M., Witte, A. V., Schroeter, M. L., Villringer, A., & Bazin, P. (2017) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x17740501 · Cited by 175 · see citing works
Rabinovici, G. D. (2019) · Continuum · doi:10.1212/con.0000000000000700 · Cited by 170 · see citing works
Sudre, C. H., Cardoso, M. J., Bouvy, W. H., Biessels, G. J., Barnes, J., & Sebastien Ourselin, A. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2419072 · Cited by 162 · see citing works
Self-citationThe Neurovascular Unit Dysfunction in Alzheimer’s Disease
Soto-Rojas, L. O., Pacheco‐Herrero, M., Martínez-Gómez, P. A., Campa-Córdoba, B. B., Apátiga-Pérez, R., Villegas-Rojas, M. M., Harrington, C. R., de la Cruz, F., Garcés‐Ramírez, L., & Luna‐Muñoz, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22042022 · Cited by 140 · see citing works
Associations between amyloid β and white matter hyperintensities: A systematic review
Roseborough, A. D., Ramirez, J., Black, S. E., & Edwards, J. D. (2017) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2017.01.026 · Cited by 134 · see citing works
Amyloid burden and metabolic function in early-onset Alzheimer’s disease: parietal lobe involvement.
Ossenkoppele, R., Zwan, M. D., Tolboom, N., van Assema, D. M. E., Adriaanse, S. F., Kloet, R. W., Boellaard, R., Windhorst, A. D., Barkhof, F., Lammertsma, A. A., et al. (2012) · Brain · doi:10.1093/brain/aws113 · Cited by 123 · see citing works
Self-citationLehmann, M., Madison, C. M., Ghosh, P. M., Seeley, W. W., Mormino, E., Greicius, M. D., Gorno-Tempini, M. L., Kramer, J. H., Miller, B. L., Jagust, W. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221536110 · Cited by 122 · see citing works
Posterior white matter disease distribution as a predictor of amyloid angiopathy
Thanprasertsuk, S., Martínez‐Ramírez, S., Pontes‐Neto, O. M., Ni, J., Ayres, A. M., Reed, A. K., Swords, K. E., Gurol, M. E., Greenberg, S. M., & Viswanathan, A. (2014) · Neurology · doi:10.1212/wnl.0000000000000732 · Cited by 118 · see citing works
Duan, J., Wang, H., Xu, J., Lin, X. Z., Chen, S., Kang, Z., & Yao, Z. (2006) · Surgical and Radiologic Anatomy · doi:10.1007/s00276-006-0111-2 · Cited by 115 · see citing works
European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study (EPAD LCS): study protocol
Solomon, A., Kivipelto, M., Molinuevo, J. L., Tom, B. D. M., & Ritchie, C. W. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-021017 · Cited by 103 · see citing works
Multitracer model for staging cortical amyloid deposition using PET imaging
Collij, L. E., Heeman, F., Salvadó, G., Ingala, S., Altomare, D., de Wilde, A., Konijnenberg, E., van Buchem, M. M., Yaqub, M. M., Markiewicz, P. J., et al. (2020) · Neurology · doi:10.1212/wnl.0000000000010256 · Cited by 98 · see citing works
Self-citationMcAleese, K. E., Miah, M., Graham, S., Hadfield, G. M., Walker, L., Johnson, M. A., Colloby, S. J., Thomas, A. J., DeCarli, C. S., Koss, D. J., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02376-2 · Cited by 95 · see citing works
Brain amyloid and vascular risk are related to distinct white matter hyperintensity patterns
Pålhaugen, L., Sudre, C. H., Tecelao, S., Nakling, A., Almdahl, I. S., Kalheim, L. F., Cardoso, M. J., Johnsen, S. H., Rongve, A., Aarsland, D., et al. (2021) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20957604 · Cited by 79 · see citing works
Self-citationThe EMIF-AD PreclinAD study: study design and baseline cohort overview
Konijnenberg, E., Carter, S. F., ten Kate, M., den Braber, A., Tomassen, J., Amadi, C., Wesselman, L. M. P., Nguyen, H., van de Kreeke, J. A., Yaqub, M. M., et al. (2018) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0406-7 · Cited by 75 · see citing works
Self-citationDistribution of white matter hyperintensity in cerebral hemorrhage and healthy aging
Zhu, Y., Chabriat, H., Godin, O., Dufouil, C., Rosand, J. M., Greenberg, S. M., Smith, E. E., Tzourio, C., & Viswanathan, A. (2011) · Journal of Neurology · doi:10.1007/s00415-011-6218-3 · Cited by 73 · see citing works
Study, F. T. A., Salvadó, G., Brugulat‐Serrat, A., Sudre, C. H., Grau‐Rivera, O., Suárez‐Calvet, M., Falcón, C., Fauria, K., Cardoso, M. J., Barkhof, F., et al. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0460-1 · Cited by 71 · see citing works
Self-citationCollij, L. E., Salvadó, G., Shekari, M., Alves, I. L., Reimand, J., Wink, A. M., Zwan, M. D., Niñerola‐Baizán, A., Perissinotti, A., Scheltens, P., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05174-2 · Cited by 69 · see citing works
Self-citationQuantitative amyloid PET in Alzheimer's disease: the AMYPAD prognostic and natural history study
Alves, I. L., Collij, L. E., Altomare, D., Frisoni, G. B., Saint‐Aubert, L., Payoux, P., Kivipelto, M., Jessen, F., Drzezga, A. E., Leeuwis, A. E., et al. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12069 · Cited by 68 · see citing works
Self-citationAltered intrinsic and extrinsic connectivity in schizophrenia
Zhou, Y., Zeidman, P., Wu, S., Razi, A., Cheng Ann, C. H. E. N., Yang, L., Zou, J., Wang, G., Wang, H., & Friston, K. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.12.006 · Cited by 66 · see citing works
Study, F. T. A., Brugulat-Serrat, A., Salvadó, G., Sudre, C. H., Grau-Rivera, O., Suárez-Calvet, M., Falcon, C., Sánchez-Benavides, G., Gramunt, N., Fauria, K., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00151-2 · Cited by 64 · see citing works
Self-citationBullseye's representation of cerebral white matter hyperintensities
Sudre, C. H., Gómez‐Ansón, B., Davagnanam, I., Schmitt, A., Mendelson, A., Prados, F., Smith, L. A., Atkinson, D., Hughes, A. D., Chaturvedi, N., et al. (2017) · Journal of Neuroradiology · doi:10.1016/j.neurad.2017.10.001 · Cited by 53 · see citing works
Self-citationWhite matter hyperintensities are associated with subthreshold amyloid accumulation
Moscoso, A., Rey-Bretal, D., Silva-Rodríguez, J., Aldrey, J. M., Cortés, J., Pías-Peleteiro, J., Ruibal, Á., & Aguiar, P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116944 · Cited by 49 · see citing works
Calvin, C. M., de Boer, C., Raymont, V., Gallacher, J., & Koychev, I. G. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00711-5 · Cited by 43 · see citing works
Brain amyloid β, cerebral small vessel disease, and cognition
Saridin, F. N., Hilal, S., Villaraza, S. G., Reilhac, A., Gyanwali, B., Tanaka, T., Stephenson, M. C., Ng, S. L., Vrooman, H. A., van der Flier, W. M., et al. (2020) · Neurology · doi:10.1212/wnl.0000000000011029 · Cited by 40 · see citing works
Clinical Features and Experimental Models of Cerebral Small Vessel Disease
Shindo, A., Ishikawa, H., Ii, Y., Niwa, A., & Tomimoto, H. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00109 · Cited by 34 · see citing works
Lorenzini, L., Ingala, S., Wink, A. M., Kuijer, J. P. A., Wottschel, V., Dijsselhof, M. B. J., Sudre, C. H., Haller, S., Molinuevo, J. L., Gispert, J. D., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103106 · Cited by 34 · see citing works
Self-citationLow Prevalence and Clinical Effect of Vascular Risk Factors in Early-Onset Alzheimer’s Disease
Chen, Y. S., Lille YOD study group BomboisStéphanieDr. BoutantinJustine CassagnaudPascalineDr. ChenYaohuaDr. DelbeuckXavier DelmaireChristineDr. DeramecourtVincentProf. GelePatrick Houssein-FoucherClaudeDr. JacquemontCharlotte LebertFlorenceDr. LebouvierThibaudDr. LopezRenaud MackowiakMarie-AnneDr. MaureilleAurélien PasquierFlorenceProf. PetytGrégoryDr. PolletMarianne Rollin-SillaireAdelineDr. SchraenSusannaDr. SemahFranckProf. VanhoutteMatthieu, Sillaire, A. R., Dallongeville, J., Skrobala, E., Wallon, D., Dubois, B., Hannequin, D., & Pasquier, F. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170367 · Cited by 31 · see citing works
White matter hyperintensities and vascular risk factors in monozygotic twins
ten Kate, M., Sudre, C. H., den Braber, A., Konijnenberg, E., Nivard, M. G., Cardoso, M. J., Scheltens, P., Sebastien Ourselin, A., Boomsma, D. I., Barkhof, F., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.02.002 · Cited by 25 · see citing works
Self-citationClinical Implications of Amyloid-Beta Accumulation in Occipital Lobes in Alzheimer’s Continuum
Hwang, J., Kim, C. M., Kim, J. E., et al. (2021) · Brain Sciences · doi:10.3390/brainsci11091232 · Cited by 18 · see citing works
Neurovascular unit dysfunction in Alzheimer’s disease
Soto-Rojas, L. O., Pacheco‐Herrero, M., de la Cruz‐López, F., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.050442 · Cited by 10 · see citing works
The European Prevention of Alzheimer’s Dementia (EPAD) MRI Dataset and Processing WorkflowPreprint
Lorenzini, L., Ingala, S., Wink, A. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.29.462349 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A review of neuroimaging-based data-driven approach for Alzheimer’s disease heterogeneity analysis
Liu, L., Sun, S., Kang, W., Wu, S., & Lin, L. (2024) · Reviews in the Neurosciences · doi:10.1515/revneuro-2023-0033 · Cited by 16 · see citing works
Ali, D. G., Abner, E. L., Bahrani, A. A., El Khouli, R., Gold, B. T., Jiang, Y., Wilcock, D. M., & Jicha, G. A. (2023) · Brain Sciences · doi:10.3390/brainsci13020218 · Cited by 7 · see citing works
“Additionally, evidence from recent studies has suggested that the relationship between WMHs and Aβ is strongly determined by the spatial distribution of the two pathologies [24].”
Background
- Authors
- Lorenzini, L., Ansems, L. T., Lopes Alves, I., Ingala, S., Vállez García, D., Tomassen, J., Sudre, C., Salvadó, G., Shekari, M., Operto, G., Brugulat-Serrat, A., Sánchez-Benavides, G., ten Kate, M., Tijms, B., Wink, A. M., Mutsaerts, H. J. M. M., den Braber, A., Visser, P. J., van Berckel, B. N. M., Gispert, J. D., Barkhof, F., Collij, L. E., the EPAD consortium for the ALFA cohort, Beteta, A., Brugulat, A., Cacciaglia, R., Cañas, A., Deulofeu, C., Cumplido, I., Dominguez, R., Emilio, M., Fauria, K., Fuentes, S., Hernandez, L., Huesa, G., Huguet, J., Marne, P., Menchón, T., Polo, A., Pradas, S., Rodriguez-Fernandez, B., Sala-Vila, A., Sánchez-Benavides, G., Soteras, A., & Vilanova, M.
- Affiliations
- Amsterdam Neuroscience (NL)
- Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience , Amsterdam , The Netherlands
- King's College London (GB)
- Pasqual Maragall Foundation (ES)
- Universitat Pompeu Fabra (ES)
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation , Barcelona , Spain
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC , Amsterdam , The Netherlands
- Maastricht University (NL)
- University of California, San Francisco (US)
- Corresponding author
- Lorenzini, Luigi
- ORCID
- Lorenzini, Luigi
- Ingala, Silvia
- Vállez García, David
- Tomassen, Jori
- Sudre, Carole
- Salvadó, Gemma
- Shekari, Mahnaz
- Tijms, Betty
- Wink, Alle Meije
- Mutsaerts, Henk J M M
- Visser, Pieter Jelle
- Gispert, Juan Domingo
- Barkhof, Frederik
- Collij, Lyduine E
- Cacciaglia, Raffaele
- Cañas, Alba
- Fauria, Karine
- Huguet, Jordi
- Soteras, Anna
- Venue
- Brain Communications
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-05-02
- Volume
- 4
- Issue
- 3
- Pages
- fcac150-fcac150
- ISSN
- 2632-1297 (ISSN-L)
- PMC
- PMC9246276
- OpenAlex
- W4282958471
- Cited by
- 36
- References
- 64
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 8 · 2023: 8 · 2022: 1
- FWCI
- 5.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Innovative Medicines Initiative (115952, 115736)
- European Union
- EFPIA
- ALFA
- “la Caixa” Foundation (100010434)
- Alzheimer’s Association
- TriBEKa Imaging Platform
- Universities and Research Secretariat
- Ministry of Business and Knowledge of the Catalan Government
- EPAD
- EU/EFPIA
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- white matter hyperintensities, cortical amyloid, Alzheimer's disease, preclinical Alzheimer's disease, vascular risk factors, cognitive performance, memory, precuneus, cuneus, medial orbitofrontal cortex, centiloid, APOE ε4, non-demented elderly
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
