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Structural MRI biomarkers for preclinical and mild Alzheimer's disease
Open accessAbstract
Noninvasive MRI biomarkers for Alzheimer's disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. To assess potential neuroimaging biomarkers, the Alzheimer's Disease Neuroimaging Initiative is following normal controls (NC) and individuals with mild cognitive impairment (MCI) or AD. We applied high‐throughput image analyses procedures to these data to demonstrate the feasibility of detecting subtle structural changes in prodromal AD. Raw DICOM scans (139 NC, 175 MCI, and 84 AD) were downloaded for analysis. Volumetric segmentation and cortical surface reconstruction produced continuous cortical surface maps and region‐of‐interest (ROI) measures. The MCI cohort was subdivided into single‐ (SMCI) and multiple‐domain MCI (MMCI) based on neuropsychological performance. Repeated measures analyses of covariance were used to examine group and hemispheric effects while controlling for age, sex, and, for volumetric measures, intracranial vault. ROI analyses showed group differences for ventricular, temporal, posterior and rostral anterior cingulate, posterior parietal, and frontal regions. SMCI and NC differed within temporal, rostral posterior cingulate, inferior parietal, precuneus, and caudal midfrontal regions. With MMCI and AD, greater differences were evident in these regions and additional frontal and retrosplenial cortices; evidence for non‐AD pathology in MMCI also was suggested. Mesial temporal right‐dominant asymmetries were evident and did not interact with diagnosis. Our findings demonstrate that high‐throughput methods provide numerous measures to detect subtle effects of prodromal AD, suggesting early and later stages of the preclinical state in this cross‐sectional sample. These methods will enable a more complete longitudinal characterization and allow us to identify changes that are predictive of conversion to AD. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.
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“it has been shown that brain atrophy as revealed by structural MRI is able to discriminate preclinical stages and time evolution of AD at 1‐year follow‐up. 34 , 35 , 36”
From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [34]
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,417
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324
Self-citationCortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Self-citationFischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Self-citationMild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
Self-citationMeasuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,995
Self-citationDiagnosis and management of dementia with Lewy bodies: Third report of the DLB consortium
McKeith, I. G., Dickson, D. W., Lowe, J., Emre, M., O'Brien, J. T., Feldman, H., Cummings, J., Duda, J. E., Lippa, C., Perry, E. K., et al. (2005) · Neurology · doi:10.1212/01.wnl.0000187889.17253.b1 · Cited by 5,069
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Tondelli, M., Wilcock, G. K., Nichelli, P., de Jager, C. A., Jenkinson, M., & Zamboni, G. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.05.018 · Cited by 265
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Alberdi, A., Aztiria, A., & Basarab, A. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.06.003 · Cited by 231
Pereira, J. B., Ibarretxe‐Bilbao, N., Martı́, M. J., Compta, Y., Junqué, C., Bargalló, N., & TOLOSA, E. S. (2011) · Human Brain Mapping · doi:10.1002/hbm.21378 · Cited by 234
Fjell, A. M., Walhovd, K. B., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., Dale, A. M., & Initiative, A. D. N. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.3785-09.2010 · Cited by 211
Self-citationMild Cognitive Impairment: Diagnosis, Longitudinal Course, and Emerging Treatments
Vega, J. N., & NEWHOUSE, P. A. (2014) · Current Psychiatry Reports · doi:10.1007/s11920-014-0490-8 · Cited by 211
Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., & Ye, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.059 · Cited by 195
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Abutalebi, J., Canini, M., Rosa, P. A. D., Green, D. W., & Weekes, B. (2014) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2014.09.008 · Cited by 188
Abrol, A., Bhattarai, M., Fedorov, A., Du, Y., Plis, S., Calhoun, V. D., & Initiative, F. T. A. D. N. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108701 · Cited by 194
Douaud, G., Menke, R. A. L., Gass, A., Monsch, A. U., Rao, A., Whitcher, B., Zamboni, G., Matthews, P. M., Sollberger, M., & Smith, S. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4437-12.2013 · Cited by 180
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Ledig, C., Schuh, A., Guerrero, R., Heckemann, R. A., & Rueckert, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-29295-9 · Cited by 175
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Self-citationCrous-Bou, M., Gascon, M., Gispert, J. D., Cirach, M., Sánchez-Benavides, G., Falcon, C., Arenaza-Urquijo, E. M., Gotsens, X., Fauria, K., Sunyer, J., et al. (2020) · Environment International · doi:10.1016/j.envint.2020.105546 · Cited by 145
Tang, X., Holland, D., Dale, A. M., Laurent Younes, B., Miller, M. I., & Initiative, A. D. N. (2014) · Human Brain Mapping · doi:10.1002/hbm.22431 · Cited by 148
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Chang, Y.-L., Jacobson, M. W., Fennema-Notestine, C., Hagler, D. J., Jennings, R. G., Dale, A. M., McEvoy, L. K., & Initiative, A. A. D. N. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp192 · Cited by 130
Self-citationPrediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data
Li, K., Chan, W., Doody, R. S., Quinn, J., Luo, S., & Initiative, T. A. D. N. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-161201 · Cited by 126
Karow, D. S., McEvoy, L. K., Fennema‐Notestine, C., Hagler, D. J., Jennings, R. G., Brewer, J. B., Hoh, C. K., Dale, A. M., & Initiative, A. D. N. (2010) · Radiology · doi:10.1148/radiol.10091402 · Cited by 120
Self-citationA comparison of FreeSurfer-generated data with and without manual intervention
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Neural Correlates of Spatial Navigation Changes in Mild Cognitive Impairment and Alzheimer’s Disease
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Karunamuni, R. A., Bartsch, H., White, N. S., Moiseenko, V., Carmona, R., Marshall, D. C., Seibert, T. M., McDonald, C. R., Farid, N., Krishnan, A., et al. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.10.026 · Cited by 118
Self-citationA generative probability model of joint label fusion for multi-atlas based brain segmentation
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Self-citationLim, H. K., Jung, W. S., Ahn, K. J., Won, W. Y., Hahn, C., Lee, S. Y., Kim, I. S., & Lee, C. U. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.264 · Cited by 110
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Self-citationVisser, P. J., Legdeur, N., ten Kate, M., & Bertens, D. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780198701590.003.0125 · Cited by 106
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Self-citationChincarini, A., Sensi, F., Rei, L., Gemme, G., Squarcia, S., Longo, R., Brun, F., Tangaro, S., Bellotti, R., Amoroso, N., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.065 · Cited by 94
Rossini, P. M., Miraglia, F., & Vecchio, F. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12645 · Cited by 93
Zhao, Q., Sang, X., Metmer, H., Swati, Z. N. K., & Lu, J. (2019) · Cortex · doi:10.1016/j.cortex.2019.04.026 · Cited by 93
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Self-citationGeneralized Scalar-on-Image Regression Models via Total Variation
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Chang, Y., Bondi, M. W., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Jacobson, M. W., & Dale, A. M. (2009) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2009.12.024 · Cited by 92
Self-citationVolume reduction in subcortical regions according to severity of Alzheimer’s disease
Roh, J. H., Qiu, A., Seo, S. W., Soon, H. W., Kim, J., Kim, G. H., Kim, M., Lee, J. M., & Na, D. L. (2011) · Journal of Neurology · doi:10.1007/s00415-010-5872-1 · Cited by 88
Initiative, A. D. N., Huang, K., Lin, Y., Yang, L., Wang, Y., Cai, S., Pang, L., Wu, X., & Huang, L. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0551-0 · Cited by 85
The Role of Clusterin in Amyloid-β–Associated Neurodegeneration
Desikan, R. S., Thompson, W. K., Holland, D., Hess, C. P., Brewer, J. B., Zetterberg, H., Blennow, K., Andreassen, O. A., McEvoy, L. K., Hyman, B. T., et al. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2013.4560 · Cited by 79
Self-citationRizvi, B., Lao, P. J., Chesebro, A. G., Dworkin, J. D., Amarante, E., Beato, J. M., Gutierrez, J., Zahodne, L. B., Schupf, N., Manly, J. J., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.25166 · Cited by 79
Moodley, K. K., Minati, L., Contarino, V. E., Prioni, S., Wood, R., Cooper, R., D’Incerti, L., Tagliavini, F., & Chan, D. (2015) · Hippocampus · doi:10.1002/hipo.22417 · Cited by 79
Alzheimer's disease markers, hypertension, and gray matter damage in normal elderly
Glodzik, L., Mosconi, L., Tsui, W., Santi, S. D., Zinkowski, R. P., Pirraglia, E., Rich, K. E., McHugh, P. F., Li, Y., Williams, S., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.02.012 · Cited by 79
Zhao, Q., Lu, H., Metmer, H., Li, X., & Lu, J. (2017) · Brain Research · doi:10.1016/j.brainres.2017.10.025 · Cited by 77
Six-month atrophy in MTL structures is associated with subsequent memory decline in elderly controls
Murphy, E. A., Holland, D., Donohue, M., McEvoy, L. K., Hagler, D. J., Dale, A. M., & Brewer, J. B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.016 · Cited by 79
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Holland, D., McEvoy, L. K., Desikan, R. S., Dale, A. M., & Initiative, A. D. N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047739 · Cited by 71
Self-citationDeficit in Central Auditory Processing as a Biomarker of Pre-Clinical Alzheimer’s Disease
Tuwaig, M., Savard, M., Jutras, B., Poirier, J., Collins, D. L., Rosa‐Neto, P., Fontaine, D. J., & Breitner, J. C. S. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170545 · Cited by 72
Spampinato, M. V., Rumboldt, Z., Hosker, R. J., Mintzer, J. E., & Initiative, A. D. N. (2010) · Radiology · doi:10.1148/radiol.10100307 · Cited by 68
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Peña‐Casanova, J., Sánchez‐Benavides, G., de Sola, S., Manero-Borràs, R. M., & Casals-Coll, M. (2012) · Archives of Medical Research · doi:10.1016/j.arcmed.2012.08.015 · Cited by 65
Nettiksimmons, J., Harvey, D. J., Brewer, J. B., Carmichael, O. T., DeCarli, C. S., Jack, C. R., Petersen, R. C., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.025 · Cited by 64
Fujishima, M., Maikusa, N., Nakamura, K., Nakatsuka, M., Matsuda, H., & Meguro, K. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00306 · Cited by 68
Rebsamen, M., Rummel, C., Reyes, M., Wiest, R., & McKinley, R. (2020) · Human Brain Mapping · doi:10.1002/hbm.25159 · Cited by 64
Hypertension-Related Alterations in White Matter Microstructure Detectable in Middle Age
McEvoy, L. K., Fennema‐Notestine, C., Eyler, L. T., Franz, C. E., Hagler, D. J., Lyons, M. J., Panizzon, M. S., Rinker, D. A., Dale, A. M., & Kremen, W. S. (2015) · Hypertension · doi:10.1161/hypertensionaha.115.05336 · Cited by 65
Self-citationSpulber, G., Simmons, A., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H., Spenger, C., Lovestone, S., et al. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12028 · Cited by 63
Cui, Y. J., Wen, W. X., Lipnicki, D. M., Beg, M. F., Jin, J. S., Luo, S., Zhu, W., Kochan, N. A., Reppermund, S., Zhuang, L., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.013 · Cited by 62
Khan, T. K., & Alkon, D. L. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150238 · Cited by 62
Radiomic analysis in prediction of Human Papilloma Virus status
Yu, K., Zhang, Y., Yu, Y. Y., Huang, C., Liu, R., Li, T., Yang, L., Morris, J. S., Baladandayuthapani, V., & Hongtu Zhu, B. (2017) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2017.10.001 · Cited by 61
Brain size and the compensation of Alzheimer's disease symptoms: A longitudinal cohort study
Guo, L., Alexopoulos, P., Wagenpfeil, S., Kurz, A., Perneczky, R., & Initiative, A. D. N. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.10.002 · Cited by 66
Evidence of Altered Corticomotor System Connectivity in Early-Stage Alzheimer's Disease
Vidoni, E. D., Thomas, G., Honea, R. A., Loskutova, N., & Burns, J. M. (2012) · Neurology Report · doi:10.1097/npt.0b013e3182462ea6 · Cited by 57
Structural Neuroimaging in the Detection and Prognosis of Pre-Clinical and Early AD
Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Jacobson, M. W., Dale, A. M., & Initiative, T. A. D. N. (2009) · Behavioural Neurology · doi:10.1155/2009/698156 · Cited by 58
Self-citationA review of neuroimaging biomarkers of Alzheimer's disease.
Varghese, T., Sheelakumari, R., James, J. S., & Mathuranath, P. S. (2013) · PubMed · Cited by 57
Morys, F., Initiative, A. D. N., Potvin, O., Zeighami, Y., Vogel, J. W., Lamontagne‐Caron, R., Duchesne, S., & Dagher, A. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220535 · Cited by 57
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., & Ye, J. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339710 · Cited by 53
Initiative, A. D. N., Stricker, N. H., Dodge, H. H., Dowling, N. M., Han, S. D., Erosheva, E. A., & Jagust, W. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9171-6 · Cited by 56
Olsson, E., Klasson, N., Berge, J., Eckerström, C., Edman, Å., Malmgren, H., & Wallin, A. (2013) · Journal of Aging Research · doi:10.1155/2013/198471 · Cited by 51
Thong, J. Y. J., Du, J., Ratnarajah, N., Dong, Y., Soon, H. W., Saini, M., Tan, M., Tuan, T. A., Chen, C., & Qiu, A. (2013) · Human Brain Mapping · doi:10.1002/hbm.22330 · Cited by 49
Neuroimaging Results Impose New Views on Alzheimer’s Disease—the Role of Amyloid Revised
Fjell, A. M., & Walhovd, K. B. (2011) · Molecular Neurobiology · doi:10.1007/s12035-011-8228-7 · Cited by 48
A practical guideline for intracranial volume estimation in patients with Alzheimer's disease
Sargolzaei, S., Sargolzaei, A., Cabrerizo, M., Chen, G., Goryawala, M., Noei, S., Zhou, Q., Duara, R., Barker, W., & Adjouadi, M. (2015) · BMC Bioinformatics · doi:10.1186/1471-2105-16-s7-s8 · Cited by 46
Neuroimaging Enrichment Strategy for Secondary Prevention Trials in Alzheimer Disease
McEvoy, L. K., Edland, S. D., Holland, D., Hagler, D. J., Roddey, J. C., Fennema‐Notestine, C., Salmon, D. P., Koyama, A. K., Aisen, P. S. S., Brewer, J. B., et al. (2010) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3181d1b814 · Cited by 45
Self-citationZhao, H., Li, X., Wu, W., Li, Z., Qian, L., Li, S., Zhang, B., & Xu, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130017 · Cited by 43
Li, H., Liu, Y., Gong, P., Zhang, C., Ye, J., & Initiative, A. D. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0082450 · Cited by 42
An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automatic Prognosis of MCI Patients
Aksu, Y., Miller, D. J., Kesidis, G., Bigler, D. C., & Yang, Q. X. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025074 · Cited by 42
Lee, E., Hongtu Zhu, B., Kong, D., Wang, Y., Giovanello, K. S., Ibrahim, J. G., & Initiative, A. D. N. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas879 · Cited by 42
Initiative, A. D. N., Petrone, P., Casamitjana, A., Falcón, C., Artigues, M., Operto, G., Cacciaglia, R., Molinuevo, J. L., Vilaplana, V., & Gispert, J. D. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0526-8 · Cited by 42
Yamaguchi, S., Murakami, T., Satoh, M., Komiyama, T., Ohi, T., Miyoshi, Y., Endo, K., Hiratsuka, T., Hara, A., Tatsumi, Y., et al. (2023) · Neurology · doi:10.1212/wnl.0000000000207579 · Cited by 42
Estimating Intracranial Volume in Brain Research: An Evaluation of Methods
Sargolzaei, S., Sargolzaei, A., Cabrerizo, M., Chen, G., Goryawala, M., Pinzon-Ardila, A., Gonzalez‐Arias, S., & Adjouadi, M. (2015) · Neuroinformatics · doi:10.1007/s12021-015-9266-5 · Cited by 42
Multiplex Networks for Early Diagnosis of Alzheimer's Disease
Amoroso, N., Rocca, M. L., Bruno, S., Maggipinto, T., Monaco, A., Bellotti, R., & Tangaro, S. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00365 · Cited by 59
Chang, Y., Fennema‐Notestine, C., Holland, D., McEvoy, L. K., Stricker, N. H., Salmon, D. P., Dale, A. M., Bondi, M. W., & Initiative, A. D. N. (2014) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.05.1763 · Cited by 39
Self-citationVolumetric and Diffusion Abnormalities in Subcortical Nuclei of Older Adults With Cognitive Frailty
Wan, M., Xia, R., Lin, H., Qiu, P., He, J., Ye, Y., Tao, J., Chen, L., & Zheng, G. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00202 · Cited by 38
Sánchez‐Benavides, G., for the NEURONORMA Study Team, Peña‐Casanova, J., Casals-Coll, M., Gramunt, N., Molinuevo, J. L., Gómez‐Ansón, B., Aguilar, M., Robles, A., Antúnez, C., et al. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-132186 · Cited by 39
Qi, H., Liu, H., Hu, H., He, H., & Zhao, X. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00344 · Cited by 36
Panizzon, M. S., Fennema‐Notestine, C., Kubarych, T. S., Chen, C., Eyler, L. T., Fischl, B. R., Franz, C. E., Grant, M. D., Hamza, S., Jak, A. J., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.122 · Cited by 35
Self-citation
Metadata
- Authors
- Fennema‐Notestine, C., Hagler, D. J., McEvoy, L. K., Fleisher, A. S., Wu, E. H., Karow, D. S., & Dale, A. M.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Fennema‐Notestine, Christine
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2009-03-10
- Volume
- 30
- Issue
- 10
- Pages
- 3238-3253
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC2951116
- OpenAlex
- W2079502798
- Cited by
- 233
- References
- 75
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 6 · 2023: 9 · 2022: 9 · 2021: 11 · 2020: 10 · 2019: 9 · 2018: 13 · 2017: 11 · 2016: 13 · 2015: 18 · 2014: 18 · 2013: 22 · 2012: 23
- FWCI
- 14.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Foundation for the National Institutes of Health
- National Institutes of Health (5u01ag024904-10)
- National Institute on Aging (U01-AG-024904, U19AG010483)
- National Center for Research Resources (U24RR021382)
- 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, mild cognitive impairment, preclinical Alzheimer's disease, structural MRI biomarkers, 3D image segmentation, cortical surface reconstruction, region of interest analysis, hemispheric lateralization, medial temporal lobe, conversion to Alzheimer's disease, high-throughput image analysis, Alzheimer's Disease Neuroimaging Initiative
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cerebral Cortex, Cognition Disorders, Female, Humans, Image Processing, Computer-Assisted, Functional Laterality, Magnetic Resonance Imaging, Male, Mental Status Schedule, Middle Aged, Neuropsychological Tests, Cohort Studies, Case-Control Studies
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