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

Cited by (1,039)

Items 301–400 of 1,039

1. An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis
   Song, Juan, Zheng, Jian, Li, Ping, Lu, Xiaoyuan, Zhu, Guangming, Shen, Peiyi · 2021 · Frontiers in Digital Health
2. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease
   Katherine R. Gray, Robin Wolz, Rolf A. Heckemann, Paul Aljabar, Alexander Hammers, Daniel Rueckert · 2012 · NeuroImage
3. A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia
   Jessica A. Turner · 2013 · Frontiers in Neuroscience
4. Brain Structure in Young and Old East Asians and Westerners: Comparisons of Structural Volume and Cortical Thickness
   Michael W.L. Chee, Hui Zheng, Joshua Oon Soo Goh, Denise C. Park, Bradley P. Sutton · 2010 · Journal of Cognitive Neuroscience
5. COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets
   Adam Scott, William Courtney, Dylan Wood, Raul de la Garza, Susan Lane, Margaret D. King, Runtang Wang, Jody Roberts, Jessica A. Turner, Vince D. Calhoun · 2011 · Frontiers in Neuroinformatics
6. Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers
   Aoyan Dong, Jon B. Toledo, Nicolas Honnorat, Jimit J. Doshi, Erdem Varol, Aristeidis Sotiras, David A. Wolk, John Q. Trojanowski, Christos A. Davatzikos, Alzheimer’s Disease Neuroimaging Initiative · 2016 · Brain
7. Chronic divalproex sodium use and brain atrophy in Alzheimer disease
   Adam Fleisher, Diana Truran, J.T. Mai, Jessica B. S. Langbaum, Paul S. S. Aisen, Jeffrey L. Cummings, Clifford R. Jack, Michael Walter Weiner, Ronald G. Thomas, Lon S. Schneider et al. · 2011 · Neurology
8. Factors affecting Aβ plasma levels and their utility as biomarkers in ADNI
   Jon B. Toledo, Hugo Vanderstichele, Michal Figurski, Paul S. S Aisen, Ronald Carl Petersen, Michael Walter Weiner, Clifford R. Jack, William Jagust, Charles S DeCarli, Arthur W. Toga et al. · 2011 · Acta Neuropathologica
9. Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders
   Yasser Iturria‐Medina, Roberto C. Sotero, Paule Joanne Toussaint, Alan Charles Evans, and the Alzheimer's Disease Neuroimaging Initiative · 2014 · PLoS Computational Biology
10. Plasma ceramides are altered in mild cognitive impairment and predict cognitive decline and hippocampal volume loss
    Mielke, Michelle M., Haughey, Norman J., Bandaru, Veera Venkata Ratnam, Schech, Steven, Carrick, Richard, Carlson, Michelle C., Mori, Susumu, Miller, Michael I., Ceritoglu, Can, Brown, Timothy et al. · 2010 · Alzheimer's & Dementia
11. Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification
    Chong-yaw Wee A, Pew‐Thian Yap, Daoqiang Zhang, Lihong Wang, Dinggang G. Shen · 2013 · Brain Structure and Function
12. Mitochondrial dysfunction and cell death in neurodegenerative diseases through nitroxidative stress
    Mohammed Danish Akbar, Musthafa Mohamed Essa, Ghazi Daradkeh, Mohamed A. Abdelmegeed, Youngshim Choi, Lubna Mahmood, Byoung‐Joon Song · 2016 · Brain Research
13. Polygenic risk of Alzheimer disease is associated with early- and late-life processes
    Elizabeth C. Mormino, Reisa Anne Sperling, Avram J. Holmes, Randy L. Buckner, Philip L. De Jager, Jordan W. Smoller, Mert Rory Sabuncu, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Neuroimaging Initiative, Michael Walter Weiner et al. · 2016 · Neurology
14. One is the deadliest number: the detrimental effects of social isolation on cerebrovascular diseases and cognition
    Brett D. Friedler, Joshua D. Crapser, Louise D. McCullough · 2014 · Acta Neuropathologica
15. Fractals in the Neurosciences, Part II
    Antonio Di Ieva, Francisco José Esteban, Fabio Grizzi, Włodzimierz Klonowski, Miguel Martı́n-Landrove · 2013 · The Neuroscientist
16. VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer’s disease prediction
    Zhentao Hu, Zheng Wang, Yong Jin, Wei Hou · 2022 · Computer Methods and Programs in Biomedicine
17. A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond
    Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian, Shalini Subramanian, Aaron Carass, Jerry L. Prince, Yong Du · 2024 · Medical Image Analysis
18. Breakdown of Brain Connectivity Between Normal Aging and Alzheimer's Disease: A Structural <i>k</i> -Core Network Analysis
    Daianu, Madelaine, Jahanshad, Neda, Nir, Talia M., Toga, Arthur W., Jack, Clifford R., Weiner, Michael W., Thompson, for the Alzheimer's Disea, Paul M. · 2013 · Brain Connectivity
19. Depressive Symptoms in Mild Cognitive Impairment Predict Greater Atrophy in Alzheimer's Disease-Related Regions
    Grace J. Lee, Po H. Lu, Xue Hua, Suh Lee, Stephanie Wu, Ken Nguyen, Edmond Teng, Alex Leow, Clifford R. Jack, Arthur W. Toga et al. · 2012 · Biological Psychiatry
20. Quantifying performance of machine learning methods for neuroimaging data
    Lee Jollans, Rory Thomas Boyle, Eric Artiges A, Tobias Banaschewski, Sylvane Desrivières, Antoine Grigis, Jean‐Luc Martinot, Tomáš Paus, Michael N. Smolka, Henrik Walter et al. · 2019 · NeuroImage
21. Measurement of MRI scanner performance with the ADNI phantom
    Jeffrey L. Gunter, Matt A. Bernstein, Bret Borowski, Chadwick P. Ward, Paula J. Britson, Joel P. Felmlee, Norbert W. Schuff, Michael Walter Weiner, Clifford R. Jack · 2009 · Medical Physics
22. GAN-based synthetic brain PET image generation
    Jyoti Islam, Yanqing Zhang · 2020 · Brain Informatics
23. Automatic quality assessment in structural brain magnetic resonance imaging
    Bénédicte Mortamet, Matt A. Bernstein, Clifford R. Jack, Jeffrey L. Gunter, Chadwick P. Ward, Paula J. Britson, Reto Antoine Meuli, Jean‐Philippe Thiran, Gunnar Krüger · 2009 · Magnetic Resonance in Medicine
24. Associations Between Alzheimer Disease Biomarkers, Neurodegeneration, and Cognition in Cognitively Normal Older People
    Wirth, Miranka, Villeneuve, Sylvia, Haase, Claudia M., Madison, Cindee M., Oh, Hwamee, Landau, Susan M., Rabinovici, Gil D., Jagust, William J. · 2013 · JAMA Neurology
25. MicroRNA Expression Levels Are Altered in the Cerebrospinal Fluid of Patients with Young-Onset Alzheimer’s Disease
    Paul M. McKeever, Raphaël Schneider, Foad Taghdiri, Anna Weichert, Namita Multani, Robert A. Brown, Adam L. Boxer, Anna Karydas, Bruce L. Miller, Janice Robertson et al. · 2018 · Molecular Neurobiology
26. Cortical iron reflects severity of Alzheimer's disease
    van Duijn, Sara, Bulk, Marjolein, van Duinen, Sjoerd G., Nabuurs, Rob J.A., van Buchem, Mark A., van der Weerd, Louise, Natté, Remco · 2017 · Journal of Alzheimer s Disease
27. Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics
    C. Schwarz, Robert I. Reid, J. Gunter, M. Senjem, S. Przybelski, S. Zuk, J. Whitwell, P. Vemuri, K. Josephs, K. Kantarci et al. · 2014 · NeuroImage
28. 3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry
    Xue Hua, Alex Leow, Suh Lee, Andrea D. Klunder, Arthur W. Toga, Natasha Leporé, Yi-Yu Chou, Caroline Brun, Mark Yi-Hsuan Chiang, Marina Barysheva A · 2008 · NeuroImage
29. Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data
    Jorge Samper‐Gonzàlez, Ninon Burgos, Simona Bottani, Sabrina Fontanella, Pascal Lu, Arnaud Marcoux, Alexandre Routier, Jérémy Guillon, Michael Bacci, Junhao Wen et al. · 2018 · NeuroImage
30. Characterization of Brain Volume Changes in Aging Individuals With Normal Cognition Using Serial Magnetic Resonance Imaging
    Shohei Fujita, Susumu Mori, Kengo Onda, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, T. Yoshikawa, Hidemasa Takao, Naoto Hayashi, Osamu Abe · 2023 · JAMA Network Open
31. Walking ability to predict future cognitive decline in old adults: A scoping review
    Lisette H. J. Kikkert, Nicolas Vuillerme, Jos P. van Campen, Tibor Hortobágyi, Claudine J. C. Lamoth · 2016 · Ageing Research Reviews
32. Heterogeneous patterns of brain atrophy in Alzheimer's disease
    Konstantinos Poulakis, Joana B. Pereira, Patrizia Mecocci, Bruno J. Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilkka S. Soininen, Simon Lovestone, Andrew Simmons, Lars‐Olof Wahlund et al. · 2018 · Neurobiology of Aging
33. A multiomics approach to heterogeneity in Alzheimer’s disease: focused review and roadmap
    AmanPreet Badhwar, G. Peggy McFall, Shraddha Sapkota, Sandra E. Black, Howard Mark Chertkow, Simon Duchesne, Mario Masellis, Liang Li, Roger A. Dixon, Pierre Bellec · 2019 · Brain
34. Association of cognitive impairment, depressive symptoms and sarcopenia among healthy older men in the veterans retirement community in southern T aiwan: A cross‐sectional study
    Ying‐Hsin Hsu, Chih‐Kuang Liang, Ming-Yueh Chou, Mei-Chen Liao, Yu‐Teh Lin, Liang‐Kung Chen, Yuk‐Keung Lo · 2014 · Geriatrics and gerontology international/Geriatrics & gerontology international
35. Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion
    Neil K. Savalia, Phillip F Agres, Micaela Y. Chan, Eric Feczko, Kristen M. Kennedy, Gagan S. Wig · 2016 · Human Brain Mapping
36. Trajectories of imaging markers in brain aging: the Rotterdam Study
    Elisabeth J. Vinke, Marius de Groot, Vikram Venkatraghavan, Stefan Klein, Wiro J. Niessen, M. Arfan Ikram, Meike Willemijn Vernooij · 2018 · Neurobiology of Aging
37. Clinical prediction from structural brain MRI scans: A large-scale empirical study
    for the Alzheimer’s Disease Neuroimaging Initiative, Sabuncu, Mert R., Konukoglu, Ender · 2015 · Neuroinformatics
38. Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort
    Frithjof Kruggel, Jessica A. Turner, L. Tugan Muftuler · 2009 · NeuroImage
39. Cerebral atrophy in mild cognitive impairment and Alzheimer disease
    Kelvin K. Leung, Jonathan William Bartlett, Josephine Barnes, Emily Ndakola Manning, Sebastien Ourselin A, Nick C. Fox, Alzheimer’s Disease Neuroimaging Initiative · 2013 · Neurology
40. Cellular correlates of cortical thinning throughout the lifespan
    Vidal-Pineiro, Didac, Parker, Nadine, Shin, Jean, French, Leon, Grydeland, Håkon, Jackowski, Andrea P., Mowinckel, Athanasia M., Patel, Yash, Pausova, Zdenka, Salum, Giovanni et al. · 2020 · Scientific Reports
41. Synaptic Change in the Posterior Cingulate Gyrus in the Progression of Alzheimer's Disease
    Stephen W. Scheff, DOUGLAS A. PRICE, Mubeen A. Ansari, Kelly N. Roberts, Frederick A. Schmitt, Miloš D. Ikonomović, Elliott Jay Mufson · 2014 · Journal of Alzheimer s Disease
42. Locally linear embedding (LLE) for MRI based Alzheimer's disease classification
    Xin Liu, Duygu Tosun, Michael Walter Weiner, Norbert W. Schuff · 2013 · NeuroImage
43. Clinical and multimodal biomarker correlates of ADNI neuropathological findings
    Jon B. Toledo, Nigel J. Cairns, Xiao Da, Kewei Chen, Deborah R. Carter, Adam Fleisher, Erin Householder, Napatkamon Ayutyanont, Auttawut Roontiva, Robert J. Bauer III et al. · 2013 · Acta Neuropathologica Communications
44. Test–retest reliability of freesurfer measurements within and between sites: Effects of visual approval process
    Zafer İşcan, Tony B. Jin, Alexandria Kendrick, Bryan C. Szeglin, Hanzhang Lu, Madhukar Trivedi, Maurizio Fava M. D, Patrick J. McGrath, Myrna M. Weissman, Benji T. Kurian et al. · 2015 · Human Brain Mapping
45. Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis
    The Alzheimer’s Disease Neuroimaging Initiative, Heung‐Il Suk, Seong–Whan Lee, Dinggang G. Shen · 2015 · Brain Structure and Function
46. Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review
    Parvathy Jyothi, A. Robert Singh · 2022 · Artificial Intelligence Review
47. Diabetes and Elevated Hemoglobin A1c Levels Are Associated with Brain Hypometabolism but Not Amyloid Accumulation
    Rosebud O. Roberts, David S. Knopman, Ruth H. Cha, Michelle M. Mielke, V. Shane Pankratz, Bradley F. Boeve, Kejal Kantarci, Yonas E. Geda, Clifford R. Jack, Ronald Carl Petersen et al. · 2014 · Journal of Nuclear Medicine
48. A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease
    Tong Tong, Qinquan Gao, Ricardo Guerrero, Christian Ledig, Liang Chen, Daniel Rueckert, Alzheimer’s Disease Neuroimaging Initiative · 2016 · IEEE Transactions on Biomedical Engineering
49. Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI
    Ali Khazaee, Ata Ebrahimzadeh, Abbas Babajani-feremi A · 2016 · Behavioural Brain Research
50. Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis
    Mingxia Liu, Jun Zhang, Dong Nie, Pew‐Thian Yap, Dinggang G. Shen · 2018 · IEEE Journal of Biomedical and Health Informatics
51. Reduced coupling between cerebrospinal fluid flow and global brain activity is linked to Alzheimer disease–related pathology
    Feng Han, Jing Chen, Aaron Belkin-Rosen, Yameng Gu, Liying Luo, Orfeu Marcello Buxton, Xiao Liu, Alzheimer’s Disease Neuroimaging Initiative · 2021 · PLoS Biology
52. Characterizing Alzheimer's disease using a hypometabolic convergence index
    Kewei Chen, Napatkamon Ayutyanont, Jessica B. S. Langbaum, Adam Fleisher, Cole Reschke, Wendy Lee, Xiaofen Liu, Dan Bandy, Gene E. Alexander, Paul M. Thompson et al. · 2011 · NeuroImage
53. Inflammatory Proteins in Plasma Are Associated with Severity of Alzheimer’s Disease
    Leung, Rufina, Proitsi, Petroula, Simmons, Andrew, Lunnon, Katie, Güntert, Andreas, Kronenberg, Deborah, Pritchard, Megan, Tsolaki, Magda, Mecocci, Patrizia, Kloszewska, Iwona et al. · 2013 · PLoS ONE
54. View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data
    Mingxia Liu, Jun Zhang, Pew‐Thian Yap, Dinggang G. Shen · 2016 · Medical Image Analysis
55. Basal forebrain volume reliably predicts the cortical spread of Alzheimer’s degeneration
    Sara Fernández‐Cabello, Martin Kronbichler, Koene R. A. Van Dijk, James A Goodman, R. Nathan Spreng, Taylor W. Schmitz, on behalf of the Alzheimer’s Disease Neuroimaging Initiative · 2020 · Brain
56. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years
    Danai Dima, Amirhossein Modabbernia, Efstathios Papachristou, Gaelle Eve Doucet, Ingrid Agartz, Moji Aghajani, Theophilus N. Akudjedu, Anton Albajes‐Eizagirre, Dag Alnæs, Kathryn I. Alpert et al. · 2021 · Human Brain Mapping
57. Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection
    Kelvin K. Leung, Matthew J. Clarkson, Jonathan William Bartlett, Shona Clegg, Clifford R. Jack, Michael Walter Weiner, Nick C. Fox, Sebastien Ourselin A · 2009 · NeuroImage
58. Type 2 diabetes mellitus, brain atrophy, and cognitive decline
    Chris Moran, Richard J Beare, Wei Wang, Michele L. Callisaya, Velandai K. Srikanth, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Neuroimaging Initiative, Michael Walter Weiner, Paul Aisen, Michael Walter Weiner et al. · 2019 · Neurology
59. Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer’s disease
    Furney, S J, Simmons, A, Breen, G, Pedroso, I, Lunnon, K, Proitsi, P, Hodges, A, Powell, J, Wahlund, L-O, Kloszewska, I et al. · 2011 · Molecular Psychiatry
60. Trail Making Test: Regression-based Norms for the Portuguese Population
    Sara Cavaco, Alexandra Gonçalves, Claudia Auxiliadora Pinto, Eduarda Almeida, F. Gomes, Ines C. Moreira, Joana Fernandes, Armando M Teixeira-Pinto · 2013 · Archives of Clinical Neuropsychology
61. Pooling fMRI data: meta-analysis, mega-analysis and multi-center studies
    Sergi G. Costafreda · 2009 · Frontiers in Neuroinformatics
62. Progressive dysexecutive syndrome due to Alzheimer's disease: a description of 55 cases and comparison to other phenotypes
    Townley, Ryan A, Graff-Radford, Jonathan, Mantyh, William G, Botha, Hugo, Polsinelli, Angelina J, Przybelski, Scott A, Machulda, Mary M, Makhlouf, Ahmed T, Senjem, Matthew L, Murray, Melissa E et al. · 2020 · Brain Communications
63. Vascular burden and Alzheimer disease pathologic progression
    Lo, Raymond Y., Jagust, William J., Weiner, Michael, Aisen, Paul, Weiner, Michael, Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jagust, William, Trojanowki, John Q. et al. · 2012 · Neurology
64. Morphometric and Histologic Substrates of Cingulate Integrity in Elders with Exceptional Memory Capacity
    Tamar D. Gefen, Melanie Peterson, Steven T. Papastefan, Adam Martersteck, Kristen R. Whitney, ALFRED W. RADEMAKER, Eileen H. Bigio, Sandra Weıntraub, Emily Joy Rogalski, Marsel M. Mesulam et al. · 2015 · Journal of Neuroscience
65. Anxiety and cognition
    Erin A. Maloney, Jason R. Sattizahn, Sian Leah Beilock · 2014 · Wiley Interdisciplinary Reviews Cognitive Science
66. Reducing hippocampal extracellular matrix reverses early memory deficits in a mouse model of Alzheimer’s disease
    Marlene J. Végh, Céline M Heldring, Willem Kamphuis, Sara Hijazi, Arie J. Timmerman, Ka Wan Li, Pim van Nierop, Huibert D. Mansvelder, Elly M. Hol, August Benjamin Smit et al. · 2014 · Acta Neuropathologica Communications
67. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI
    Seyed Hani Hojjati, Ata Ebrahimzadeh, Ali Khazaee, Abbas Babajani-feremi A · 2018 · Computers in Biology and Medicine
68. Alzheimer's Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition
    Leow, Alex D., Yanovsky, Igor, Parikshak, Neelroop, Hua, Xue, Lee, Suh, Toga, Arthur W., Jack, Clifford R., Bernstein, Matt A., Britson, Paula J., Gunter, Jeffrey L. et al. · 2009 · NeuroImage
69. Cognitive reserve and Alzheimer's disease biomarkers are independent determinants of cognition
    Prashanthi Vemuri, Stephen D. Weigand, Scott A. Przybelski, David S. Knopman, Glenn E. Smith, John Q. Trojanowski, Leslie M. J. Shaw, Charles S DeCarli, Owen Thomas Carmichael, Matt A. Bernstein et al. · 2011 · Brain
70. Current progress, challenges and future prospects of diagnostic and therapeutic interventions in Alzheimer's disease
    Rajasekhar, K., Govindaraju, Thimmaiah · 2018 · RSC Advances
71. A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease
    Fan Li, Manhua Liu · 2019 · Journal of Neuroscience Methods
72. Alzheimer's Disease Neurodegenerative Biomarkers Are Associated with Decreased Cognitive Function but Not β-Amyloid in Cognitively Normal Older Individuals
    Wirth, Miranka, Madison, Cindee M., Rabinovici, Gil D., Oh, Hwamee, Landau, Susan M., Jagust, William J. · 2013 · The Journal of Neuroscience
73. False positives in neuroimaging genetics using voxel-based morphometry data
    Silver, Matt, Montana, Giovanni, Nichols, Thomas E. · 2011 · NeuroImage
74. Cortical tau load is associated with white matter hyperintensities
    McAleese, Kirsty E., Firbank, Michael, Dey, Madhurima, Colloby, Sean J., Walker, Lauren, Johnson, Mary, Beverley, Joshua R., Taylor, John Paul, Thomas, Alan J., O’Brien, John T. et al. · 2015 · Acta Neuropathologica Communications
75. In Vivo Structural Neuroanatomy of Corpus Callosum in Alzheimer's Disease and Mild Cognitive Impairment Using Different MRI Techniques: A Review
    Margherita Di Paola, Gianfranco Spalletta, Carlo Caltagirone · 2010 · Journal of Alzheimer s Disease
76. Cyclic nucleotide signaling changes associated with normal aging and age-related diseases of the brain
    Michy P. Kelly · 2017 · Cellular Signalling
77. Anxiety Symptoms in Amnestic Mild Cognitive Impairment Are Associated with Medial Temporal Atrophy and Predict Conversion to Alzheimer Disease
    Linda Mah, Malcolm A. Binns, David C. Steffens · 2014 · American Journal of Geriatric Psychiatry
78. Biomarker-based prediction of progression in MCI: comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau
    Bradford Clark Dickerson · 2013 · Frontiers in Aging Neuroscience
79. Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline
    Mara ten Kate, Ellen Dicks, Pieter Jelle Visser, Wiesje M. van der Flier, Charlotte Elisabeth Teunissen, Frederik Barkhof, Philip Scheltens, Betty Marije Tijms, Alzheimer’s Disease Neuroimaging Initiative · 2018 · Brain
80. Increased brain atrophy rates in cognitively normal older adults with low cerebrospinal fluid Aβ1‐42
    Jonathan M. Schott, Jonathan W. Bartlett, Nick C. Fox, Josephine Barnes, Alzheimer’s Disease Neuroimaging Initiative · 2010 · Annals of Neurology
81. Removing inter-subject technical variability in magnetic resonance imaging studies
    Jean‐Philippe Fortin, Elizabeth M. Sweeney, John Muschelli, Ciprian M. Crainiceanu, Russell Taki Shinohara · 2016 · NeuroImage
82. Cognitive reserve and clinical progression in Alzheimer disease
    Anna Catharina van Loenhoud, Wiesje M. van der Flier, Alle Meije Wink, Ellen Dicks, Colin Groot, Jos W. R. Twisk, Frederik Barkhof, Philip Scheltens, Rik Ossenkoppele, Alzheimer’s Disease Neuroimaging Initiative · 2019 · Neurology
83. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
    Alexandra-Maria Tăuţan, Bogdan Emanuel Ionescu, Emiliano Santarnecchi · 2021 · Artificial Intelligence in Medicine
84. The dynamics of Alzheimer's disease biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort
    Anna Caroli, Giovanni Battista Frisoni · 2010 · Neurobiology of Aging
85. Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations
    Dinov, Ivo D., Heavner, Ben, Tang, Ming, Glusman, Gustavo, Chard, Kyle, Darcy, Mike, Madduri, Ravi, Pa, Judy, Spino, Cathie, Kesselman, Carl et al. · 2016 · PLOS ONE
86. A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling
    Hampel, H., O’Bryant, S. E., Durrleman, S., Younesi, E., Rojkova, K., Escott-Price, V., Corvol, J-C., Broich, K., Dubois, B., Lista, S. et al. · 2017 · Climacteric
87. The Vascular Hypothesis of Alzheimer’s Disease: A Key to Preclinical Prediction of Dementia Using Neuroimaging
    Jack de la Torre · 2018 · Journal of Alzheimer s Disease
88. Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review
    Zhen Zhao, Joon Huang Chuah, Khin Wee Lai, Chee‐Onn Chow, Munkhjargal Gochoo, Samiappan Dhanalakshmi, Na Wang, Wei Bao, Xiang Wu · 2023 · Frontiers in Computational Neuroscience
89. Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer’s disease
    Stein, Jason L., Hua, Xue, Morra, Jonathan H., Lee, Suh, Hibar, Derrek P., Ho, April J., Leow, Alex D., Toga, Arthur W., Sul, Jae Hoon, Kang, Hyun Min et al. · 2010 · NeuroImage
90. Neuroimaging Biomarkers of Neurodegenerative Diseases and Dementia
    Shannon L. Risacher, Andrew J. Saykin · 2013 · Seminars in Neurology
91. Magnetic Resonance Image Example-Based Contrast Synthesis
    Snehashis Roy, Aaron Carass, Jerry L. Prince · 2013 · IEEE Transactions on Medical Imaging
92. The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI)
    Shannon L. Risacher · 2013 · Frontiers in Aging Neuroscience
93. Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology
    Rohit Bhargava, Anant Madabhushi · 2016 · Annual Review of Biomedical Engineering
94. Bridging Integrator 1 (BIN1) Protein Expression Increases in the Alzheimer's Disease Brain and Correlates with Neurofibrillary Tangle Pathology
    Holler, Christopher J., Davis, Paulina R., Beckett, Tina L., Platt, Thomas L., Webb, Robin L., Head, Elizabeth, Murphy, M. Paul · 2014 · Journal of Alzheimer’s Disease
95. Shapes of the Trajectories of 5 Major Biomarkers of Alzheimer Disease
    Jack, Clifford R., Vemuri, Prashanthi, Wiste, Heather J., Weigand, Stephen D., Lesnick, Timothy G., Lowe, Val, Kantarci, Kejal, Bernstein, Matt A., Senjem, Matthew L., Gunter, Jeffrey L. et al. · 2012 · Archives of Neurology
96. Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects
    Hua, Xue, Lee, Suh, Yanovsky, Igor, Leow, Alex D., Chou, Yi-Yu, Ho, April J., Gutman, Boris, Toga, Arthur W., Jack, Clifford R., Bernstein, Matt A. et al. · 2009 · NeuroImage
97. Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis
    Mingxia Liu, Daoqiang Zhang, Ehsan Adeli, Dinggang G. Shen · 2015 · IEEE Transactions on Biomedical Engineering
98. Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions
    Kurt G. Schilling, Alessandro Daducci, Klaus Hermann Maier-Hein, Cyril Poupon, Jean‐Christophe Houde, Vishwesh Nath, Adam W. Anderson, Bennett A. Landman, Maxime Descoteaux · 2018 · Magnetic Resonance Imaging
99. A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI
    Ahmad Waleed Salehi, Preety Baglat, Brij Bhushan Sharma, Gaurav Gupta, Ankita Upadhya · 2020 · International Conference on Smart Electronics and Communication (ICOSEC)
100. Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI
     Guilherme Folego, Marina Weiler, Raphael Fernandes Casseb, Ramon Pires, Anderson De Rezende Rocha · 2020 · Frontiers in Bioengineering and Biotechnology