Cited by 6Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Imaging Techniques and Applications

MRAβ : A multimodal MRI ‐derived amyloid‐β biomarker for Alzheimer's disease

Open access
Zhang, Yu, Li, Xi, Ji, Yi, Ding, Hao, Suo, Xinjun, He, Xiaoxi, Xie, Yingying, Liang, Meng +3 more·2023·Human Brain Mapping

Abstract

F (AV45), a highly sensitive and specific positron emission tomographic (PET) molecular biomarker binding to the amyloid-β of Alzheimer's disease (AD), is constrained by radiation and cost. We sought to combat it by combining multimodal magnetic resonance imaging (MRI) images and a collaborative generative adversarial networks model (CollaGAN) to develop a multimodal MRI-derived Amyloid-β (MRAβ) biomarker. We collected multimodal MRI and PET AV45 data of 380 qualified participants from the ADNI dataset and 64 subjects from OASIS3 dataset. A five-fold cross-validation CollaGAN were applied to generate MRAβ. In the ADNI dataset, we found MRAβ could characterize the subject-level AV45 spatial variations in both AD and mild cognitive impairment (MCI). Voxel-wise two-sample t-tests demonstrated amyloid-β depositions identified by MRAβ in AD and MCI were significantly higher than healthy controls (HCs) in widespread cortices (p < .05, corrected) and were much similar to those by AV45 (r > .92, p < .001). Moreover, a 3D ResNet classifier demonstrated that MRAβ was comparable to AV45 in discriminating AD from HC in both the ADNI and OASIS3 datasets, and in discriminate MCI from HC in ADNI. Finally, we found MRAβ could mimic cortical hyper-AV45 in HCs who later converted to MCI (r = .79, p < .001) and was comparable to AV45 in discriminating them from stable HC (p > .05). In summary, our work illustrates that MRAβ synthesized by multimodal MRI could mimic the cerebral amyloid-β depositions like AV45 and lends credence to the feasibility of advancing MRI toward molecular-explainable biomarkers.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/hbm.26452

Cites

6 of 6 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Alzheimer's disease is characterized by neuritic plaques and neurofibrillary tangles resulting from amyloid-beta accumulation, aggregation of fibrillar plaques, and hyperphosphorylation of tau protein.

    “Previous studies of AD pathology have demonstrated that the disease is characterized by the presence of neuritic plaques and neurofibrillary tangles resulting from amyloid-beta (Aβ) accumulation, aggregation of fibrillar plaques, and hyperphosphorylation of tau protein (Depp et al., 2023; Zhang et al., 2023; Kovacs et al., 2024; Wang et al., 2024; Zhang et al., 2025).”

    From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Zhang et al., 2023

    The Introduction describes extracellular amyloid-β plaques and intracellular tau tangles as hallmark pathologies of Alzheimer’s disease. It does not specify that the plaques are neuritic or fibrillar, or explain the tangles through tau hyperphosphorylation. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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

Flagged first, then by citations

Research works printed but not linked: 49

  1. Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009). Multi‐atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, 46(3), 726– 738. 19245840
  2. Alzheimer's Association. (2012). 2012 Alzheimer's disease facts and figures. Alzheimers & Dementia, 8(2), 131– 168. 22404854
  3. Apostolopoulos, I. D., Papathanasiou, N. D., Apostolopoulos, D. J., & Panayiotakis, G. S. (2022). Applications of generative adversarial networks (GANs) in positron emission tomography (PET) imaging: A review. European Journal of Nuclear Medicine and Molecular Imaging, 49, 1– 23. 35451611
  4. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95– 113. 17761438
  5. Chang, S., Park, S., Yang, J., & Kwak, N. (2019). Sym‐parameterized dynamic inference for mixed‐domain image translation. Paper presented at the 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
  6. Choi, H., Jin, K. H., & Alzheimer's Disease Neuroimaging, I. (2018). Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behavioural Brain Research, 344, 103– 109. 10.1016/j.bbr.2018.02.017 29454006 · DOI
  7. Choi, S. R., Golding, G., Zhuang, Z., Zhang, W., Lim, N., Hefti, F., Benedum, T. E., Kilbourn, M. R., Skovronsky, D., & Kung, H. F. (2009). Preclinical properties of 18F‐AV‐45: A PET agent for Abeta plaques in the brain. Journal of Nuclear Medicine, 50(11), 1887– 1894. 10.2967/jnumed.109.065284 19837759 PMC3065020 · DOI
  8. Clark, C. M., Pontecorvo, M. J., Beach, T. G., Bedell, B. J., Coleman, R. E., Doraiswamy, P. M., Fleisher, A. S., Reiman, E. M., Sabbagh, M. N., Sadowsky, C. H., Schneider, J. A., Arora, A., Carpenter, A. P., Flitter, M. L., Joshi, A. D., Krautkramer, M. J., Lu, M., Mintun, M. A., Skovronsky, D. M., & AV‐45‐A16 Study Group. (2012). Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid‐β plaques: A prospective cohort study. The Lancet Neurology, 11(8), 669– 678. 10.1016/s1474-4422(12)70142-4 22749065 · DOI · Paper page
  9. Clark, C. M., Schneider, J. A., Bedell, B. J., Beach, T. G., Bilker, W. B., Mintun, M. A., Pontecorvo, M. J., Hefti, F., Carpenter, A. P., Flitter, M. L., Krautkramer, M. J., Kung, H. F., Coleman, R. E., Doraiswamy, P. M., Fleisher, A. S., Sabbagh, M. N., Sadowsky, C. H., Reiman, E. P., Zehntner, S. P., … AV45‐A07 Study Group. (2011). Use of florbetapir‐PET for imaging beta‐amyloid pathology. JAMA, 305(3), 275– 283. 10.1001/jama.2010.2008 21245183 PMC7041965 · DOI · Paper page
  10. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37– 46.
  11. Doraiswamy, P. M., Sperling, R. A., Coleman, R. E., Johnson, K. A., Reiman, E. M., Davis, M. D., Grundman, M., Sabbagh, M. N., Sadowsky, C. H., Fleisher, A. S., Carpenter, A., Clark, C. M., Joshi, A. D., Mintun, M. A., Skovronsky, D. M., Pontecorvo, M. J., & For the AV45‐A11 Study Group. (2012). Amyloid‐beta assessed by florbetapir F 18 PET and 18‐month cognitive decline: A multicenter study. Neurology, 79(16), 1636– 1644. 10.1212/WNL.0b013e3182661f74 22786606 PMC3468774 · DOI
  12. Feng, F., Wang, P., Zhao, K., Zhou, B., Yao, H., Meng, Q., Wang, L., Zhang, Z., Ding, Y., Wang, L., An, N., Zhang, X., & Liu, Y. (2018). Radiomic features of hippocampal subregions in Alzheimer's disease and amnestic mild cognitive impairment. Frontiers in Aging Neuroscience, 10, 290. 10.3389/fnagi.2018.00290 30319396 PMC6167420 · DOI
  13. Fleisher, A. S., Chen, K., Liu, X., Roontiva, A., Thiyyagura, P., Ayutyanont, N., Joshi, A. D., Clark, C. M., Mintun, M. A., Pontecorvo, M. J., Doraiswamy, P. M., Johnson, K. A., Skovronsky, D. M., & Reiman, E. M. (2011). Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Archives of Neurology, 68(11), 1404– 1411. 10.1001/archneurol.2011.150 21747008 · DOI · Paper page
  14. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “ Mini‐mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189– 198. 1202204
  15. Goodfellow, I., Pouget‐Abadie, J., Mirza, M., Xu, B., Warde‐Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680. 10.48550/arXiv.1406.2661 · DOI · Paper page
  16. Grimmer, T., Faust, M., Auer, F., Alexopoulos, P., Förstl, H., Henriksen, G., Perneczky, R., Sorg, C., Yousefi, B. H., Drzezga, A., & Kurz, A. (2012). White matter hyperintensities predict amyloid increase in Alzheimer's disease. Neurobiology of Aging, 33(12), 2766– 2773. 10.1016/j.neurobiolaging.2012.01.016 22410648 · DOI
  17. Grothe, M. J., Barthel, H., Sepulcre, J., Dyrba, M., Sabri, O., & Teipel, S. J. (2017). In vivo staging of regional amyloid deposition. Neurology, 89(20), 2031– 2038. 10.1212/WNL.0000000000004643 29046362 PMC5711511 · DOI · Paper page
  18. Hognon, C., Tixier, F., Colin, T., Gallinato, O., Visvikis, D., & Jaouen, V. (2020). Influence of gradient difference loss on MR to PET brain image synthesis using GANs. Journal of Nuclear Medicine, 61, 2.
  19. Horé, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. Paper presented at the 2010 20th International Conference on Pattern Recognition.
  20. Hosseini, M., Lee, B., & Vakilinia, S. (2017). Energy performance of cool roofs under the impact of actual weather data. Energy and Buildings, 145, 284– 292. 10.1016/j.enbuild.2017.04.006 · DOI
  21. Jin, D., Zhou, B., Han, Y., Ren, J., Han, T., Liu, B., Lu, J., Song, C., Wang, P., Wang, D., Xu, J., Yang, Z., Yao, H., Yu, C., Zhao, K., Wintermark, M., Zuo, N., Zhang, X., Zhou, Y., … Liu, Y. (2020). Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer's disease. Advanced Science, 7(14), 2000675. 10.1002/advs.202000675 32714766 PMC7375255 · DOI
  22. Johnson, K. A., Sperling, R. A., Gidicsin, C. M., Carmasin, J. S., Maye, J. E., Coleman, R. E., Reiman, E. M., Sabbagh, M. N., Sadowsky, C. H., Fleisher, A. S., Murali Doraiswamy, P., Carpenter, A. P., Clark, C. M., Joshi, A. D., Lu, M., Grundman, M., Mintun, M. A., Pontecorvo, M. J., Skovronsky, D. M., & AV45‐A11 study group. (2013). Florbetapir (F18‐AV‐45) PET to assess amyloid burden in Alzheimer's disease dementia, mild cognitive impairment, and normal aging. Alzheimers Dement, 9(5 Suppl), S72– S83. 10.1016/j.jalz.2012.10.007 23375563 PMC3800236 · DOI · Paper page
  23. Kuehn, B. M. (2021). WHO: Need for global dementia care planning is urgent. JAMA, 326(15), 1471. 10.1001/jama.2021.17456 34665197 · DOI
  24. Lee, D., Kim, J., Moon, W.‐J., & Ye, J. C. (2019). CollaGAN: Collaborative GAN for missing image data imputation. Paper presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  25. Lee, D., Moon, W.‐J., & Ye, J. C. (2020). Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks. Nature Machine Intelligence, 2(1), 34– 42. 10.1038/s42256-019-0137-x · DOI
  26. Lin, W., Lin, W., Chen, G., Zhang, H., Gao, Q., Huang, Y., Tong, T., du, M., & the Alzheimer's Disease Neuroimaging Initiative. (2021). Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer's disease. Frontiers in Neuroscience, 15, 646013. 10.3389/fnins.2021.646013 33935634 PMC8080880 · DOI
  27. Mohs, R. C., Knopman, D., Petersen, R. C., Ferris, S. H., Ernesto, C., Grundman, M., Sano, M., Bieliauskas, L., Geldmacher, D., Clark, C., & Thai, L. J. (1997). Development of cognitive instruments for use in clinical trials of antidementia drugs: Additions to the Alzheimer's disease assessment scale that broaden its scope. Alzheimer Disease and Associated Disorders, 11, 13– 21. 9236948
  28. Nozadi, S. H., Kadoury, S., & The Alzheimer's Disease Neuroimaging, I. (2018). Classification of Alzheimer's and MCI patients from semantically Parcelled PET images: A comparison between AV45 and FDG‐PET. International Journal of Biomedical Imaging, 2018, 1247430. 10.1155/2018/1247430 29736165 PMC5875062 · DOI
  29. O'Bryant, S. E., Waring, S. C., Cullum, C. M., Hall, J., Lacritz, L., Massman, P. J., Lupo, P. J., Reisch, J. S., Doody, R., & Texas Alzheimer's Research Consortium. (2008). Staging dementia using clinical dementia rating scale sum of boxes scores: A Texas Alzheimer's research consortium study. Archives of Neurology, 65(8), 1091– 1095. 18695059 PMC3409562
  30. Organization, W. H. (2021). Global status report on the public health response to dementia. Reinvigorating the public health response to dementia. PMC8516159 34563278
  31. Orouskhani, M., Zhu, C., Rostamian, S., Shomal Zadeh, F., Shafiei, M., & Orouskhani, Y. (2022). Alzheimer's disease detection from structural MRI using conditional deep triplet network. Neuroscience Informatics, 2(4), 100066. 10.1016/j.neuri.2022.100066 · DOI
  32. Ossenkoppele, R., Rabinovici, G. D., Smith, R., Cho, H., Schöll, M., Strandberg, O., Palmqvist, S., Mattsson, N., Janelidze, S., Santillo, A., Ohlsson, T., Jögi, J., Tsai, R., la Joie, R., Kramer, J., Boxer, A. L., Gorno‐Tempini, M. L., Miller, B. L., Choi, J. Y., … Hansson, O. (2018). Discriminative accuracy of [18F]flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders. JAMA, 320(11), 1151– 1162. 10.1001/jama.2018.12917 30326496 PMC6233630 · DOI · Paper page
  33. Pan, Y., Liu, M., Lian, C., Xia, Y., & Shen, D. (2020). Spatially‐constrained fisher representation for brain disease identification with incomplete multi‐modal neuroimages. IEEE Transactions on Medical Imaging, 39(9), 2965– 2975. 32217472 PMC7485604
  34. Price, L. R., Deardorff, R., Wu, Y.‐C., West, J. D., McDonald, B. C., Risacher, S. L., & Saykin, A. J. (2020). A novel MRI contrast weighted ratio method for measuring myelin in older adults at risk for Alzheimer's disease. Paper presented at the 2020 Alzheimer's Association International Conference.
  35. Raber, J., Huang, Y., & Ashford, J. W. (2004). ApoE genotype accounts for the vast majority of AD risk and AD pathology. Neurobiology of Aging, 25(5), 641– 650. 10.1016/j.neurobiolaging.2003.12.023 15172743 · DOI · Paper page
  36. Rejc, L., Gómez‐Vallejo, V., Joya, A., Moreno, O., Egimendia, A., Castellnou, P., Ríos‐Anglada, X., Cossío, U., Baz, Z., Passannante, R., Tobalina‐Larrea, I., Ramos‐Cabrer, P., Giralt, A., Sastre, M., Capetillo‐Zarate, E., Košak, U., Knez, D., Gobec, S., Marder, M., … Llop, J. (2021). Longitudinal evaluation of a novel BChE PET tracer as an early in vivo biomarker in the brain of a mouse model for Alzheimer disease. Theranostics, 11(13), 6542– 6559. 10.7150/thno.54589 33995675 PMC8120209 · DOI
  37. Saint‐Aubert, L., Barbeau, E. J., Péran, P., Nemmi, F., Vervueren, C., Mirabel, H., Payoux, P., Hitzel, A., Bonneville, F., Gramada, R., Tafani, M., Vincent, C., Puel, M., Dechaumont, S., Chollet, F., & Pariente, J. (2013). Cortical florbetapir‐PET amyloid load in prodromal Alzheimer's disease patients. EJNMMI Research, 3(1), 43. 10.1186/2191-219X-3-43 23731789 PMC3733998 · DOI
  38. Smith, R. X., Strain, J. F., Tanenbaum, A., Fagan, A. M., Hassenstab, J., McDade, E., Schindler, S. E., Gordon, B. A., Xiong, C., Chhatwal, J., Jack, C., Jr., Karch, C., Berman, S., Brosch, J. R., Lah, J. J., Brickman, A. M., Cash, D. M., Fox, N. C., Graff‐Radford, N. R., … for the DIAN Network. (2021). Resting‐state functional connectivity disruption as a pathological biomarker in autosomal dominant Alzheimer disease. Brain Connectivity, 11(3), 239– 249. 10.1089/brain.2020.0808 33430685 PMC8182476 · DOI
  39. van Rooden, S., Doan, N. T., Versluis, M. J., Goos, J. D. C., Webb, A. G., Oleksik, A. M., van der Flier, W. M., Scheltens, P., Barkhof, F., Weverling–Rynsburger, A. W. E., Blauw, G. J., Reiber, J. H. C., van Buchem, M. A., Milles, J., & van der Grond, J. (2015). 7T T2*‐weighted magnetic resonance imaging reveals cortical phase differences between early‐ and late‐onset Alzheimer's disease. Neurobiology of Aging, 36(1), 20– 26. 10.1016/j.neurobiolaging.2014.07.006 25113794 · DOI
  40. van Rooden, S., Versluis, M. J., Liem, M. K., Milles, J., Maier, A. B., Oleksik, A. M., Webb, A. G., van Buchem, M. A., & van der Grond, J. (2014). Cortical phase changes in Alzheimer's disease at 7T MRI: A novel imaging marker. Alzheimer's & Dementia, 10(1), e19– e26. 10.1016/j.jalz.2013.02.002 23712002 · DOI
  41. Wearn, A. R., Nurdal, V., Saunders‐Jennings, E., Knight, M. J., Isotalus, H. K., Dillon, S., Tsivos, D., Kauppinen, R. A., & Coulthard, E. J. (2020). T2 heterogeneity: A novel marker of microstructural integrity associated with cognitive decline in people with mild cognitive impairment. Alzheimer's Research & Therapy, 12(1), 105. 10.1186/s13195-020-00672-9 PMC7488446 32912337 · DOI
  42. Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R., Jr., Jagust, W., Morris, J. C., Petersen, R. C., Salazar, J., Saykin, A. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., & Alzheimer's Disease Neuroimaging Initiative. (2017). The Alzheimer's disease neuroimaging initiative 3: Continued innovation for clinical trial improvement. Alzheimers Dement, 13(5), 561– 571. 10.1016/j.jalz.2016.10.006 27931796 PMC5536850 · DOI
  43. Weston, P. S. J., Poole, T., Nicholas, J. M., Toussaint, N., Simpson, I. J. A., Modat, M., Ryan, N. S., Liang, Y., Rossor, M. N., Schott, J. M., Ourselin, S., Zhang, H., & Fox, N. C. (2020). Measuring cortical mean diffusivity to assess early microstructural cortical change in presymptomatic familial Alzheimer's disease. Alzheimer's Research & Therapy, 12(1), 112. 10.1186/s13195-020-00679-2 PMC7499910 32943095 · DOI
  44. Xie, L., Wisse, L. E., Pluta, J., de Flores, R., Piskin, V., Manjón, J. V., Wang, H., das, S. R., Ding, S. L., Wolk, D. A., Yushkevich, P. A., & for the Alzheimer's Disease Neuroimaging Initiative. (2019). Automated segmentation of medial temporal lobe subregions on in vivo T1‐weighted MRI in early stages of Alzheimer's disease. Human Brain Mapping, 40(12), 3431– 3451. 31034738 PMC6697377
  45. Yasuno, F., Kazui, H., Morita, N., Kajimoto, K., Ihara, M., Taguchi, A., Yamamoto, A., Matsuoka, K., Takahashi, M., Nakagawara, J., Iida, H., Kishimoto, T., & Nagatsuka, K. (2017). Use of T1‐weighted/T2‐weighted magnetic resonance ratio to elucidate changes due to amyloid β accumulation in cognitively normal subjects. NeuroImage: Clinical, 13, 209– 214. 10.1016/j.nicl.2016.11.029 28003959 PMC5157788 · DOI
  46. Zhang, J., He, X., Qing, L., Gao, F., & Wang, B. (2022). BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi‐modal Alzheimer's disease diagnosis. Computer Methods and Programs in Biomedicine, 217, 106676. 10.1016/j.cmpb.2022.106676 35167997 · DOI
  47. Zhao, K., Ding, Y., Han, Y., Fan, Y., Alexander‐Bloch, A. F., Han, T., Jin, D., Liu, B., Lu, J., Song, C., Wang, P., Wang, D., Wang, Q., Xu, K., Yang, H., Yao, H., Zheng, Y., Yu, C., Zhou, B., … Multi‐Center Alzheimer Disease Imaging Consortium. (2020). Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer's disease: Diagnosis, longitudinal progress and biological basis. Science Bulletin, 65(13), 1103– 1113. 10.1016/j.scib.2020.04.003 36659162 · DOI
  48. Zhao, X., Ang, C. K. E., Acharya, U. R., & Cheong, K. H. (2021). Application of artificial intelligence techniques for the detection of Alzheimer's disease using structural MRI images. Biocybernetics and Biomedical Engineering, 41(2), 456– 473. 10.1016/j.bbe.2021.02.006 · DOI
  49. Zhou, T., Fu, H., Chen, G., Shen, J., & Shao, L. (2020). Hi‐net: Hybrid‐fusion Network for multi‐modal MR image synthesis. IEEE Transactions on Medical Imaging, 39(9), 2772– 2781. 10.1109/TMI.2020.2975344 32086202 · DOI

Cited by

Sorted by citations

Authors
Zhang, Y., Li, X., Ji, Y., Ding, H., Suo, X., He, X., Xie, Y., Liang, M., Zhang, S., Yu, C., & Qin, W.
Affiliations
  • Tianjin Medical University General Hospital (CN)
  • Shanxi Medical University (CN)
  • Tianjin Medical University (CN)
Corresponding author
Qin, Wen
Venue
Human Brain Mapping
Publisher
Wiley
Published
2023-08-14
Volume
44
Issue
15
Pages
5139-5152
ISSN
  • 1065-9471 (ISSN-L)
  • 1097-0193
OpenAlex
W4385798440
Cited by
6
References
6
Citations by year
2026: 3 · 2025: 1 · 2024: 1 · 2023: 1
FWCI
1.22
Type
journal article
Language
en
Open access
Locations
  • Human Brain Mapping (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (81601473, 81771818, 81971599, 81971694, 82030053)
  • Natural Science Foundation of Tianjin City (19JCYBJC25100)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords
amyloid-beta, Alzheimer's disease, multimodal MRI, generative adversarial networks, image generation, mild cognitive impairment, amyloid deposition, disease transformation, ADNI
MeSH terms
Alzheimer Disease, Humans, Magnetic Resonance Imaging, Biomarkers, Amyloid beta-Peptides, Positron-Emission Tomography, Cognitive Dysfunction

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