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Magnetic resonance imaging in Alzheimer’s disease neuroimaging initiative 2
Open accessAbstract
INTRODUCTION: Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS: We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS: Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION: Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
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“ADNI’s second phase (ADNI2) introduced to the initiative the use of diffusion-weighted MRI (dMRI) as an additional approach for tracking AD progression (Jack et al., 2015).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Jack et al., 2015
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“The participants recruited for ADNI3 tend to be younger and healthier, on average, than those in ADNI2, as they were recruited with the intention of studying the transition from CN to AD (Jack et al., 2015).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Jack et al., 2015
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McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
Self-citationNonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152
Self-citationSperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Self-citationHypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806
Self-citationJack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396
Self-citationThe Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Self-citationClinical and biomarker changes in dominantly inherited Alzheimer's disease
Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X., Blazey, T. M., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1202753 · Cited by 3,814
Self-citationAdvancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321
Self-citationVillemagne, 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
Research works printed but not linked: 6
- ISMRM 20th Annual Meeting and Exhibition: May 5‐11 2012; Melbourne Australia;2012:2456.
- The 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI): 2012;Nice France;2012.
- Rajagopalan P, 2013, TREM2 and neurodegenerative disease, N Engl J Med, 369, 1565
- Zhan L, 2014, MICCAI CDMRI Workshop
- Daianu M, 2014, MICCAI CDMRI
- Zhan L, 2014, 2014 IEEE 11th International Symposium Biomedical Imaging (ISBI), 234
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Waszak, M., Falkovskiy, P., Hilbert, T., Bonnier, G., Maréchal, B., Meuli, R. A., Rolf Gruetter, A., Kober, T., & Krüger, G. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26364 · Cited by 13
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Johnson, D. R., Wiste, H. J., Lowe, V. J., Schwarz, C. G., Knopman, D. S., Vemuri, P., Kantarci, K., Boeve, B. F., Graff‐Radford, J., Cogswell, P. M., et al. (2025) · Brain · doi:10.1093/brain/awaf346 · Cited by 13
Self-citationKhan, W., Amad, A., Giampietro, V., Werden, E., Simoni, S. D., O’Muircheartaigh, J., Westman, E., O’Daly, O., Williams, S., Brodtmann, A. G., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24894 · Cited by 11
Liu, M., Zhang, H., Shi, F., & Shen, D. G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_54 · Cited by 12
Yao, X., Yan, J., Ginda, M., Börner, K., Saykin, A. J., Shen, L., & Initiative, A. D. N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186095 · Cited by 12
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Self-citationLeandrou, S., Lamnisos, D., Kyriacou, P., Constanti, S., & Pattichis, C. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102098 · Cited by 11
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Phillips, S., Watt, R., Atkinson, T., Rajan, S., Hayhoe, A., Savva, G. M., Hornberger, M., Burton, B., Saada, J., Cambell-Kelly, M., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276118 · Cited by 12
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Scott, C. J. M., Arnott, S. R., Chemparathy, A., Dong, F., Solovey, I., Gee, T., Schmah, T., Chavez, S., Lobaugh, N. J., Nanayakkara, N. D., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.10.896415 · Cited by 11
Liu, T., Wang, M., Zhang, J., Ye, C., Funahashi, S., Liu, J., Wang, L., Yan, T., & Initiative, T. A. D. N. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14227 · Cited by 10
Katsumi, Y., Touroutoglou, A., Brickhouse, M. J., Eloyan, A., Eckbo, R., Zaitsev, A., Joie, R. L., Lagarde, J., Schonhaut, D. R., Thangarajah, M., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14489 · Cited by 12
Self-citationArdekani, B. A., Izadi, N. O., Hadid, S. A., Meftah, A. M., & Bachman, A. H. (2020) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2020.111107 · Cited by 10
An, H., Son, S. J., Cho, S., Cho, E. Y., Choi, B., & Kim, S. Y. (2015) · International Psychogeriatrics · doi:10.1017/s104161021500229x · Cited by 12
Bloch, L., & Friedrich, C. M. (2021) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms52027.2021.00096 · Cited by 10
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Shim, Y. (2022) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2022.21.2.59 · Cited by 9
Ramusino, M. C., Vitali, P., Anzalone, N., Melazzini, L., Lombardo, F. P., Farina, L. M., Bernini, S., & Costa, A. C. (2022) · Current Alzheimer Research · doi:10.2174/1567205019666220620112831 · Cited by 9
Wang, K., Yuan, Y., Zhu, B., Zhang, Y., Wei, Y.-F., Meng, F.-S., Zhang, S., Wang, J., Zhou, J., & Initiative, A. D. N. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1277731 · Cited by 8
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Zamani, A., Walker, A. K., Rollo, B., Ayers, K. L., Farah, R., O'Brien, T. J., & Wright, D. K. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103016 · Cited by 6
Ikanga, J. N., Hickle, S. D., Schwinne, M., Epenge, E., Gikelekele, G., Kavugho, I., Tshengele, N., Samuel, M., Zhao, L., Qiu, D., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230206 · Cited by 6
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Initiative, T. A. D. N., Ding, Z., Fleishman, G. M., Yang, X., Thompson, P. M., Kwitt, R., & Niethammer, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_31 · Cited by 6
Self-citationLee, J. S., Yoo, S., Park, S., Kim, H. J., Park, K., Seong, J., Suh, M. K., Lee, J., Jang, H., Kim, K. W., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.10.011 · Cited by 6
Ammirati, E., Moroni, F., Magnoni, M., Rocca, M. A., Messina, R., Anzalone, N., Filippis, C. D., Scotti, I., Besana, F., Spagnolo, P., et al. (2020) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2020.100619 · Cited by 6
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Liu, M., Zhang, J., Wang, Y., Zhou, Y., Xie, F. X., Guo, Q., Shi, F., Zhang, H., Wang, Q., & Shen, D. G. (2023) · iScience · doi:10.1016/j.isci.2023.108244 · Cited by 7
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Nobili, F. M., Arnaldi, D., Roccatagliata, L., Chincarini, A., Accardo, J., Picco, A., Ferrara, M., Buschiazzo, A., & Morbelli, S. D. (2014) · Springer eBooks · doi:10.1007/978-3-642-54307-4_12 · Cited by 5
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Lukacova, K., Bačiak, L., Pavukova, E., Pichová, K., Kašparová, S., & Kubíková, Ľ. (2017) · General Physiology and Biophysics · doi:10.4149/gpb_2016025 · Cited by 5
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Prakash, R. S., McKenna, M. R., Gbadeyan, O., Andridge, R., Scharre, D. W., & Initiative, A. D. N. (2020) · medRxiv · doi:10.1101/2020.10.13.20211375 · Cited by 5
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Cavallari, M., Touroutoglou, A., Katsumi, Y., Fong, T. G., Schmitt, E. M., Travison, T. G., Shafi, M. M., Libermann, T. A., Marcantonio, E. R., Alsop, D. C., et al. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.05.008 · Cited by 4
Magalhães, R. J. D. S., Marques, F., Selingue, E., Boumezbeur, F., Mériaux, S., & Sousa, N. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106679 · Cited by 4
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Viña-González, A., Gil, I. R., Pérez, S. S., Amable, N. U., Ruíz, L. P., Obaya, T. R., & Dalmau, E. G. (2021) · Neurology Perspectives · doi:10.1016/j.neurop.2021.05.005 · Cited by 4
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Shi, X., Initiative, A. D. N., Zhou, N., Sun, B., Wu, Y., Hu, Y., & Ning, Y. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220426 · Cited by 4
Modular Graph Encoding and Hierarchical Readout for Functional Brain Network Based eMCI Diagnosis
Mei, L., Liu, M., Bian, L., Zhang, Y., Shi, F., Zhang, H., & Shen, D. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21083-9_7 · Cited by 3
Guan, H., Wang, L., Yao, D., Bozoki, A., & Liu, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87589-3_2 · Cited by 4
Neuroimaging Findings in Mild Cognitive Impairment
Massa, F., Bauckneht, M., Peira, E., Lapucci, C., Picco, A., Capitanio, S., Arnaldi, D., Roccatagliata, L., Chincarini, A., & Nobili, F. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-53168-3_12 · Cited by 4
Rueda, A., & Luis Fernando Enríquez, S. (2018) · Biosalud · doi:10.17151/biosa.2018.17.2.5 · Cited by 10
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Chhetri, A., Goel, K., Ludhiadch, A., Singh, P., & Munshi, A. (2024) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/0118715273264879231027070642 · Cited by 3
Teng, J., McKenna, M. R., Gbadeyan, O., Prakash, R. S., & Initiative, A. D. N. (2024) · Network Neuroscience · doi:10.1162/netn_a_00373 · Cited by 6
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Hosseininasabnajar, F., Kakinami, L., & Initiative, A. D. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-08152-6 · Cited by 4
Mess, E., Witkowicz, M., Ornat, M., Sielski, P., & Klaszczyk, T. (2018) · Archives of Psychiatric Nursing · doi:10.1016/j.apnu.2018.03.008 · Cited by 3
Ayvaz, D. S., & Baytaş, İ. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08794 · Cited by 3
Editorial: Harmonization strategies and considerations in neuroimaging studies
Dennis, E. L., Veer, I. M., Descoteaux, M., Tate, D. F., & Wilde, E. A. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1165263 · Cited by 3
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Zhou, Z., Fischl, B. R., Aganj, I., & Initiative, A. D. N. (2025) · Human Brain Mapping · doi:10.1002/hbm.70257 · Cited by 2
Jiao, Y., Ding, Y., Luo, N., Niu, M., Zhong, M., Liu, J., Zhao, A., Li, Y., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251390836 · Cited by 3
Wu, Y., Zhang, H., Qu, J., Zhu, R., Xu, G., Xu, W., Yan, G., Yang, J., Xin, J., Li, Y., et al. (2026) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2026.111737 · Cited by 2
Menezes, J., Barbosa, M. I. A., & Rodrigues, P. M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111143 · Cited by 2
Interpretation of Biomarker Data in Diagnosis of Primary Dementias
Salardini, A. (2019) · Seminars in Neurology · doi:10.1055/s-0039-1683380 · Cited by 2
Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience
Kernbach, J. M., Ort, J., Hakvoort, K., Clusmann, H., Delev, D., & Neuloh, G. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_8 · Cited by 2
Mozdbar, S., Petersen, M. E., Zhang, F., Johnson, L. A., Tolman, A., Nyalakonda, R., Gutierrez, A., & O’Bryant, S. E. (2022) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-220070 · Cited by 2
Lee, A. J., Initiative, A. D. N., Stark, J. H., & Hayes, S. M. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231468 · Cited by 4
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By, S., Kahl, A., & Cogswell, P. M. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29462 · Cited by 2
Reconstructing physiological signals from fMRI across the adult lifespan
Wang, S., Xu, Z., Lochard, L. M., Li, Y., Fan, J., Chen, J. E., Huo, Y., Mather, M., Bayrak, R. G., & Chang, C. (2025) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3046980 · Cited by 2
Petersen, M., Coenen, M., DeCarli, C. S., Luca, A. D., van der Lelij, E. J., Initiative, A. D. N., Barkhof, F., Benke, T., Chen, C., Dal‐Bianco, P., et al. (2024) · medRxiv · doi:10.1101/2024.03.28.24305007 · Cited by 2
Self-citationKoberskaya, N. N., Андреевич, П. В., Smirnov, D. S., Гридин, В. Н., & Yakhno, N. N. (2023) · Russian neurological Journal · doi:10.30629/2658-7947-2023-28-3-22-27 · Cited by 1
DeepPhysioRecon: Tracing peripheral physiology in low frequency fMRI dynamics
Bayrak, R. G., Hansen, C. B., Salas, J. A., Ahmed, N., Lyu, I., Mather, M., Huo, Y., & Chang, C. E. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.163 · Cited by 1
Lee, A. J., Horn, O., Hayes, S. M., & Initiative, A. D. N. (2025) · Aging Neuropsychology and Cognition · doi:10.1080/13825585.2025.2562203 · Cited by 1
Dybing, K. M., Gao, S., Saykin, A. J., Risacher, S. L., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251401201 · Cited by 1
Optimizing ultra-rapid compressed-sensing MPRAGE acquisitions for brain morphometry
Hanford, L. C., Hilbert, T., Kober, T., Buckner, R. L., & Mair, R. W. (2026) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2025.1718444 · Cited by 1
Sheng, Z.-H., Chen, Y., Chen, M., Liang, S., Wang, J., Chen, Y., Li, R., Xia, B., Gou, K., Liu, S., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71122 · Cited by 1
Hallab, A., & Initiative, A. D. N. (2026) · Thyroid Research · doi:10.1186/s13044-026-00289-4 · Cited by 1
Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery
Wen, Z., Hou, B., He, W., Yang, S., Moore, J. H., Saykin, A. J., Huang, H., Thompson, P. M., Ritchie, M. D., Davatzikos, C. A., et al. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00239 · Cited by 1
Self-citationWang, Y., Song, W., Yang, X., Meng, W., Ma, Y., Wang, A., Guo, G., Zhang, Z., Li, Z., Han, H., et al. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02404-1 · Cited by 1
Liu, Y., Liu, M., Zhang, Y., & Shen, D. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_10 · Cited by 1
Tang, J., Chen, Q., Fu, Z., Liang, Y., Xu, G., Zhou, H., & He, B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26839 · Cited by 1
Lee, A. J., Howard, E., Saltiel, N. A., Hayes, J. P., & Hayes, S. M. (2024) · Brain Communications · doi:10.1093/braincomms/fcaf068 · Cited by 1
Dynamic Changes in Brain Networks of Alzheimer's Disease Based on Co-Activation Patterns
Wang, M., Ma, Y., Wu, J., Funahashi, S., Liu, T., & Yan, T. (2024) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei64163.2024.10906206 · Cited by 1
Podder, S., & Haldar, C. (2025) · AI-Driven Smart Healthcare for Society 5.0 · doi:10.1109/ieeeconf64992.2025.10962879 · Cited by 1
Dennis, E. L., Rowland, J. A., Esopenko, C., Tustison, N. J., Newsome, M. R., Hovenden, E. S., Avants, B., Gill, J., Hinds, S. R., Kenney, K., et al. (2024) · medRxiv · doi:10.1101/2024.02.27.24303460 · Cited by 1
Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer DiseasePreprint
Sahelijo, N. J., Rajagopalan, P., Lu, Q., Rahman, R., Priyadarshi, D., Goldstein, D., Thomopoulos, S. I., Bennett, D. A., Farrer, L. A., Stein, T. D., et al. (2024) · medRxiv · doi:10.1101/2024.06.21.24309255 · Cited by 1
Self-citationPatel, S. (2019) · TSpace (University of Toronto)
HIPPOCAMPAL VOLUME BASED ON MRI-VOLUMETRIC ANALYSIS IN ALZHEIMER DISEASE PATIENTS
Yakhno, N. N., Гридин, В. Н., Андреевич, П. В., Sinitsyn, V. E., Koberskaya, N. N., Smirnov, D. S., Игоревич, С. В., & Труфанов, М. И. (2020) · Russian Electronic Journal of Radiology · doi:10.21569/2222-7415-2020-10-1-50-58
Modality based Alzheimer's disease analysis: an insight
Mishra, S. D., & Dutta, M. (2020) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn51052.2020.9362747
Development of a Predictive Analytic Tool for Detection of Alzheimer’s Disease
Yee, A. H., Abdullah, A. A., & Bakar, J. A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1997/1/012004
Normalization of MRI T1w between-scan effects for improved longitudinal volumetric estimatesPreprint
Sederevičius, D., Bjørnerud, A., Walhovd, K. B., & Fjell, A. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.19.496756
Self-citationRole of Neuroimaging in Diagnosis of Alzheimer's DiseasePreprint
Ganotra, R., & Gupta, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4159097 · Cited by 1
Tracing peripheral physiology in low frequency fMRI dynamicsPreprint
Bayrak, R. G., Hansen, C. H., Salas, J. A., Ahmed, N., Lyu, I., Mather, M., Huo, Y., & Chang, C. E. (2023) · doi:10.31219/osf.io/fj4gq
Dynamic platform for magnetic resonance imaging of bioresponsive contrast agents
Perera-Gonzalez, M. (2023) · doi:10.17760/d20487197
Towards in vivo MRI-based diagnostics and tracking of dementia
Ortega‐Cruz, D. (2024) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.81334
Sex-specific clinical and neurobiological correlates of fatigue in older adults
Blasi, M. T., Alfano, A. R., Salzillo, M., Buscarnera, S., Raparelli, V., Cesari, M., Bruno, G., Canevelli, M., & Initiative, A. D. N. (2024) · GeroScience · doi:10.1007/s11357-024-01259-0 · Cited by 1
Hallab, A., & Initiative, A. D. N. (2024) · medRxiv · doi:10.1101/2024.10.02.24314757
Chan, D., Spires‐Jones, T. L., Provenzano, F. A., Glennon, E. B. C., & Duff, K. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780190065324.003.0020
Demir, E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1399-3.ch003
Genetic dissection of hippocampal sclerosis of ageing using magnetic resonance imaging surrogatesPreprint
Olivé, C., de Rojas, I., Zhang, L., Sotolongo‐Grau, Ó., Quintela, I., García‐González, P., Puerta, R., García‐Gutiérrez, F., Montrreal, L., Capdevila-Bayo, M., et al. (2025) · Research Square · doi:10.1101/2025.03.10.25323307
Dimitrov, M., Wong, N. M. L., Leaman, S., França, L. G. S., Valasakis, I., He, J. L., Lythgoe, D. J., Findon, J. L., Wichers, R., Stoencheva, V., et al. (2025) · medRxiv · doi:10.1101/2025.03.12.25323848
Hosseini, S. F., Akbarabadi, P., Noorani, F., Kazemi, D., Sadeghsalehi, H., Fattahpour, M., Sheybani‐Arani, M., Noroozi, M., Kazemi, A., & Initiative, A. D. N. (2025) · Current Alzheimer Research · doi:10.2174/0115672050379856250529113023
MRI Pulse Sequences and Techniques in Neuroimaging
Hu, P., & Ho, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-90554-4_8
Metadata
- Authors
- Jack, C. R., Barnes, J., Bernstein, M. A., Borowski, B. J., Brewer, J., Clegg, S., Dale, A. M., Carmichael, O., Ching, C., DeCarli, C., Desikan, R. S., Fennema‐Notestine, C., Fjell, A. M., Fletcher, E., Fox, N. C., Gunter, J., Gutman, B. A., Holland, D., Hua, X., Insel, P., Kantarci, K., Killiany, R. J., Krueger, G., Leung, K. K., Mackin, S., Maillard, P., Malone, I. B., Mattsson, N., McEvoy, L., Modat, M., Mueller, S., Nosheny, R., Ourselin, S., Schuff, N., Senjem, M. L., Simonson, A., Thompson, P. M., Rettmann, D., Vemuri, P., Walhovd, K., Zhao, Y., Zuk, S., & Weiner, M.
- Affiliations
- Mayo Clinic (US)
- UCL Queen Square Institute of Neurology (GB)
- University of California San Diego (US)
- University of California, Davis (US)
- University of Southern California (US)
- University of Oslo (NO)
- San Francisco VA Medical Center (US)
- Department of Anatomy and Neurobiology Boston University School of Medicine Boston MA USA
- Siemens Medical Solutions Boston MA USA
- University College London (GB)
- University of Gothenburg (SE)
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology University College London London UK
- MR Applications and Workflow GE Healthcare Rochester MN USA
- Philips Healthcare Cleveland OH USA
- Corresponding author
- Jack, Clifford R.
- ORCID
- Jack, Clifford R.
- Barnes, Josephine
- Bernstein, Matt A.
- Clegg, Shona
- Dale, Anders M.
- Carmichael, Owen
- Ching, Christopher
- DeCarli, Charles
- Desikan, Rahul S.
- Fennema‐Notestine, Christine
- Fjell, Anders M.
- Fletcher, Evan
- Fox, Nick C.
- Gutman, Boris A.
- Holland, Dominic
- Hua, Xue
- Insel, Philip
- Kantarci, Kejal
- Maillard, Pauline
- Malone, Ian B.
- Mattsson, Niklas
- McEvoy, Linda
- Mueller, Susanne
- Nosheny, Rachel
- Senjem, Matthew L.
- Thompson, Paul M.
- Rettmann, Dan
- Vemuri, Prashanthi
- Walhovd, Kristine
- Zhao, Yansong
- Weiner, Michael
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2015-07
- Volume
- 11
- Issue
- 7
- Pages
- 740-756
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC4523217
- OpenAlex
- W2156450079
- Cited by
- 220
- References
- 185
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 32 · 2023: 15 · 2022: 20 · 2021: 29 · 2020: 30 · 2019: 17 · 2018: 14 · 2017: 8 · 2016: 6 · 2015: 3
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Alzheimer s & Dementia (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute for Health and Care Research (NF-SI-0513-10134, NF-SI-0508-10123)
- National Institutes of Health (5u01ag024904-10)
- National Institute on Aging (U01-AG-024904, R01AG040060, R01AG011378, R01AG041851)
- National Institute of Mental Health (R01MH098062)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015922)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease, Alzheimer's Disease Neuroimaging Initiative
- MeSH terms
- Alzheimer Disease, Brain, Cognition Disorders, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Spin Labels, Biomarkers, Positron-Emission Tomography, History, 20th Century, History, 21st Century
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