The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods

Cited by (1,040)

Items 1,001–1,040 of 1,040

1. Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease
   Huang, Meiyan, Yang, Wei, Feng, Qianjin, Chen, Wufan, The Alzheimer’s Disease Neuroimaging Initiative, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jagust, William et al. · 2017 · Scientific Reports
2. Effects of ApoE4 and maternal history of dementia on hippocampal atrophy
   Andrawis, John P., Hwang, Kristy S., Green, Amity E., Kotlerman, Jenny, Elashoff, David, Morra, Jonathan H., Cummings, Jeffrey L., Toga, Arthur W., Thompson, Paul M., Apostolova, Liana G. · 2012 · Neurobiology of Aging
3. Effects of MRI scanner manufacturers in classification tasks with deep learning models
   Kushol, Rafsanjany, Parnianpour, Pedram, Wilman, Alan H., Kalra, Sanjay, Yang, Yee-Hong · 2023 · Scientific Reports
4. Angular versus spatial resolution trade-offs for diffusion imaging under time constraints
   Zhan, Liang, Jahanshad, Neda, Ennis, Daniel B., Jin, Yan, Bernstein, Matthew A., Borowski, Bret J., Jack, Clifford R., Toga, Arthur W., Leow, Alex D., Thompson, Paul M. · 2013 · Human Brain Mapping
5. Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes
   Xu, Zhiyuan, Shen, Xiaotong, Pan, Wei, for the Alzheimer's Disease Neuroimaging Initiative · 2014 · PLoS ONE
6. White matter hyperintensities are associated with subthreshold amyloid accumulation
   Moscoso, Alexis, Rey-Bretal, David, Silva-Rodríguez, Jesús, Aldrey, Jose M., Cortés, Julia, Pías-Peleteiro, Juan, Ruibal, Álvaro, Aguiar, Pablo · 2020 · NeuroImage
7. Complexity of MRI white matter hyperintensity assessments in relation to cognition in aging and dementia from the Sunnybrook Dementia Study
   Gao, Fu-qiang, Swartz, Richard H., Scheltens, Philip, Leibovitch, Farrell S., Kiss, Alex, Honjo, Kie, Black, Sandra E. · 2011 · Journal of Alzheimer's Disease
8. Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification
   Hett, Kilian, Ta, Vinh-Thong, Catheline, Gwenaëlle, Tourdias, Thomas, Manjón, José V., Coupé, Pierrick, Alzheimer’s Disease Neuroimaging Initiative, Weiner, Michael W., Aisen, Paul, Petersen, Ronald et al. · 2019 · Scientific Reports
9. Algorithmic fairness of machine learning models for Alzheimer disease progression
   Yuan, Chenxi, Linn, Kristin A., Hubbard, Rebecca A. · 2023 · JAMA Network Open
10. Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
    Panman, Jessica L., To, Yang Yang, van der Ende, Emma L., Poos, Jackie M., Jiskoot, Lize C., Meeter, Lieke H. H., Dopper, Elise G. P., Bouts, Mark J. R. J., van Osch, Matthias J. P., Rombouts, Serge A. R. B. et al. · 2019 · Frontiers in Neuroscience
11. Predicting Reduction of Cerebrospinal Fluid β-Amyloid 42 in Cognitively Healthy Controls
    Mattsson, Niklas, Insel, Philip S., Donohue, Michael, Jagust, William, Sperling, Reisa, Aisen, Paul, Weiner, Michael W. · 2015 · JAMA Neurology
12. Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients
    Kim, Hee-Jong, Shin, Jeong-Hyeon, Han, Cheol E., Kim, Hee Jin, Na, Duk L., Seo, Sang Won, Seong, Joon-Kyung · 2016 · Frontiers in Neuroscience
13. Comprehensive Gene- and Pathway-Based Analysis of Depressive Symptoms in Older Adults
    Nho, Kwangsik, Ramanan, Vijay K., Horgusluoglu, Emrin, Kim, Sungeun, Inlow, Mark H., Risacher, Shannon L., McDonald, Brenna C., Farlow, Martin R., Foroud, Tatiana M., Gao, Sujuan et al. · 2015 · Journal of Alzheimer’s Disease
14. Variants in PPP3R1 and MAPT are associated with more rapid functional decline in Alzheimer's disease: The Cache County Dementia Progression Study
    Peterson, David, Munger, Caitlin, Crowley, Jared, Corcoran, Chris, Cruchaga, Carlos, Goate, Alison M., Norton, Maria C., Green, Robert C., Munger, Ronald G., Breitner, John C.S. et al. · 2014 · Alzheimer's & Dementia
15. APOE interacts with age to modify rate of decline in cognitive and brain changes in Alzheimer's disease
    Chang, Yu‐Ling, Fennema‐Notestine, Christine, Holland, Dominic, McEvoy, Linda K., Stricker, Nikki H., Salmon, David P., Dale, Anders M., Bondi, Mark W., Alzheimer's Disease Neuroimaging Initiative · 2014 · Alzheimer's & Dementia
16. MRI assessment of whole-brain structural changes in aging
    Guo, Hui, Siu, William, D’Arcy, Ryan, Black, Sandra, Grajauskas, Lukas, Singh, Sonia, Zhang, Yunting, Rockwood, Kenneth, Song, Xiaowei · 2017 · Clinical Interventions in Aging
17. Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort
    Aguilar, Carlos, Muehlboeck, J-Sebastian, Mecocci, Patrizia, Vellas, Bruno, Tsolaki, Magda, Kloszewska, Iwona, Soininen, Hilkka, Lovestone, Simon, Wahlund, Lars-Olof, Simmons, Andrew et al. · 2014 · Frontiers in Aging Neuroscience
18. The Rationale and Design of the Reducing Pathology in Alzheimer’s Disease through Angiotensin TaRgeting (RADAR) Trial
    Kehoe, Patrick G., Blair, Peter S., Howden, Beth, Thomas, David L., Malone, Ian B., Horwood, Jeremy, Clement, Clare, Selman, Lucy E., Baber, Hannah, Lane, Athene et al. · 2017 · Journal of Alzheimer's Disease
19. Deep grading for MRI-based differential diagnosis of Alzheimer’s disease and frontotemporal dementia
    Nguyen, Huy-Dung, Clément, Michaël, Planche, Vincent, Mansencal, Boris, Coupé, Pierrick · 2023 · Artificial Intelligence in Medicine
20. Staging Neurodegenerative Disorders: Structural, Regional, Biomarker, and Functional Progressions
    Archer, Trevor, Kostrzewa, Richard M., Beninger, Richard J., Palomo, Tomas · 2011 · Neurotoxicity Research
21. Prediction of progression from mild cognitive impairment to Alzheimer's disease with longitudinal and multimodal data
    Ding, Huitong, Wang, Biqi, Hamel, Alexander P., Melkonyan, Mark, Ang, Ting F. A., Au, Rhoda, Lin, Honghuang · 2023 · Frontiers in Dementia
22. Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants
    Krishnan, Michelle L., Wang, Zi, Silver, Matt, Boardman, James P., Ball, Gareth, Counsell, Serena J., Walley, Andrew J., Montana, Giovanni, Edwards, Anthony David · 2016 · Brain and Behavior
23. DunedinPACNI estimates the longitudinal pace of aging from a single brain image to track health and disease
    Whitman, Ethan T., Elliott, Maxwell L., Knodt, Annchen R., Abraham, Wickliffe C., Anderson, Tim J., Cutfield, Nicholas J., Hogan, Sean, Ireland, David, Melzer, Tracy R., Ramrakha, Sandhya et al. · 2025 · Nature Aging
24. Frontostriatal functional connectivity correlates with repetitive behaviour across autism spectrum disorder and obsessive–compulsive disorder
    Akkermans, Sophie E.A., Rheinheimer, Nicole, Bruchhage, Muriel M.K., Durston, Sarah, Brandeis, Daniel, Banaschewski, Tobias, Boecker-Schlier, Regina, Wolf, Isabella, Williams, Steven C.R., Buitelaar, Jan K. et al. · 2019 · Psychological Medicine
25. Sharing Data Is Essential for the Future of AI in Medical Imaging
    Bell, Laura C., Shimron, Efrat · 2024 · Radiology: Artificial Intelligence
26. TNFRSF1B Gene Variants and Related Soluble TNFR2 Levels Impact Resilience in Alzheimer’s Disease
    Pillai, Jagan A., Bebek, Gurkan, Khrestian, Maria, Bena, James, Bergmann, Cornelia C., Bush, William S., Leverenz, James B., Bekris, Lynn M. · 2021 · Frontiers in Aging Neuroscience
27. Predicting Alzheimer’s Disease Using Combined Imaging-Whole Genome SNP Data
    Kong, Dehan, and for the Alzheimer’s Disease Neuroimaging Initiative, Giovanello, Kelly S., Wang, Yalin, Lin, Weili, Lee, Eunjee, Fan, Yong, Murali Doraiswamy, P, Zhu, Hongtu · 2015 · Journal of Alzheimer's Disease
28. Genes, pathways and risk prediction in Alzheimer’s disease
    Hardy, John, Escott-Price, Valentina · 2019 · Human Molecular Genetics
29. MKL for robust multi-modality AD classification
    Hinrichs, Chris, Singh, Vikas, Xu, Guofan, Johnson, Sterling · 2009 · Lecture Notes in Computer Science
30. Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer’s disease to Parkinson’s disease
    Ostertag, Cécilia, Visani, Muriel, Urruty, Thierry, Beurton-Aimar, Marie · 2023 · International Journal of Computer Assisted Radiology and Surgery
31. Neurocognitive Phenotypes and Genetic Dissection of Disorders of Brain and Behavior
    Congdon, Eliza, Poldrack, Russell A., Freimer, Nelson B. · 2010 · Neuron
32. Cortical amyloid accumulation is associated with alterations of structural integrity in older people with subjective memory complaints
    Teipel, Stefan J., Cavedo, Enrica, Weschke, Sarah, Grothe, Michel J., Rojkova, Katrine, Fontaine, Gaëlle, Dauphinot, Luce, Gonzalez-Escamilla, Gabriel, Potier, Marie-Claude, Bertin, Hugo et al. · 2017 · Neurobiology of Aging
33. Identifying progression-specific Alzheimer’s subtypes using multimodal transformer
    Machado Reyes, Diego, Chao, Hanqing, Hahn, Juergen, Shen, Li, Yan, Pingkun, for the Alzheimer’s Disease Neuroimaging Initiative · 2024 · Journal of Personalized Medicine
34. Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment
    Dang, Mingxi, Yang, Caishui, Chen, Kewei, Lu, Peng, Li, He, Zhang, Zhanjun, for the Beijing Aging Brain Rejuvenation Initiative, for the Alzheimer’s Disease Neuroimaging Initiative · 2023 · Alzheimer's Research & Therapy
35. The Role of Information and Communication Technologies in Clinical Trials with Patients with Alzheimer’s Disease and Related Disorders
    König, Alexandra, Sacco, Guillaume, Bensadoun, Gregory, Bremond, Francois, David, Renaud, Verhey, Frans, Aalten, Pauline, Robert, Philippe, Manera, Valeria · 2015 · Frontiers in Aging Neuroscience
36. Identification of functionally connected multi-omic biomarkers for Alzheimer’s disease using modularity-constrained Lasso
    Xie, Linhui, Varathan, Pradeep, Nho, Kwangsik, Saykin, Andrew J., Salama, Paul, Yan, Jingwen · 2020 · PLOS ONE
37. Genome-wide association study identifies SIAH3 locus influencing the rate of ventricular enlargement in non-demented elders
    Li, Xian, on behalf of Alzheimer’s Disease Neuroimaging Initiative, Chu, Shu-Guang, Shen, Xue-Ning, Hou, Xiao-He, Xu, Wei, Ou, Ya-Nan, Dong, Qiang, Tan, Lan, Yu, Jin-Tai · 2019 · Aging
38. Amyloid-PET and White Matter Hyperintensities Have Independent Effects on Baseline Cognitive Function and Synergistic Effects on Longitudinal Executive Function
    Ali, Doaa G., Abner, Erin L., Bahrani, Ahmed A., El Khouli, Riham, Gold, Brian T., Jiang, Yang, Wilcock, Donna M., Jicha, Gregory A. · 2023 · Brain Sciences
39. White matter microstructural abnormality precedes cortical volumetric decline in Alzheimer’s disease: evidence from data-driven disease progression modelling
    Parker, CS, Weston, PSJ, Zhang, H, Oxtoby, NP, the Alzheimer’s Disease Neuroimaging Initiative · 2022
40. FISMI-DRL: A Framework for Interactive Segmentation of Medical Image Based On Deep Reinforcement Learning
    Qian, Chen, Yang, Hao, Li, Jiyun · 2023 · 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)