Quantitative structural MRI for early detection of Alzheimer's disease
Open accessAbstract
Alzheimer's disease (AD) is a common progressive neurodegenerative disorder that is not currently diagnosed until a patient reaches the stage of dementia. There is a pressing need to identify AD at an earlier stage, so that treatment, when available, can begin early. Quantitative structural MRI is sensitive to the neurodegeneration that occurs in mild and preclinical AD, and is predictive of decline to dementia in individuals with mild cognitive impairment. Objective evidence of ongoing brain atrophy will be critical for risk/benefit decisions once potentially aggressive, disease-modifying treatments become available. Recent advances have paved the way for the use of quantitative structural MRI in clinical practice, and initial clinical use has been promising. However, further experience with these measures in the relatively unselected patient populations seen in clinical practice is needed to complete translation of the recent enormous advances in scientific knowledge of AD into the clinical realm.
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Regional reductions in brain volumes occur during aging, and become more pervasive with MCI and AD.
“Regional reductions occur during aging, and become more pervasive with MCI and AD [195, 196].”
From The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives (2014) · cites this paper as [196]
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Regional rates of neocortical atrophy from normal aging to early Alzheimer disease
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Positron emission tomography imaging in dementia
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MRI and CSF biomarkers in normal, MCI, and AD subjects
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Structural MRI biomarkers for preclinical and mild Alzheimer's disease
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Self-citationAmyloid Imaging in Aging and Dementia: Testing the Amyloid HypothesisIn Vivo
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Staging of Alzheimer-Related Cortical Destruction
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Subregional neuroanatomical change as a biomarker for Alzheimer's disease
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Recent developments in Alzheimer's disease therapeutics
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Differential Cortical Atrophy in Subgroups of Mild Cognitive Impairment
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Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo.
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Cerebrospinal Fluid Biomarkers for Alzheimer's Disease
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Atrophy rates accelerate in amnestic mild cognitive impairment
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A Single Ascending Dose Study of Bapineuzumab in Patients With Alzheimer Disease
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Measuring atrophy in Alzheimer disease
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Multi-modal imaging predicts memory performance in normal aging and cognitive decline
Walhovd, K. B., Fjell, A. M., Dale, A. M., McEvoy, L. K., Brewer, J. B., Karow, D. S., Salmon, D. P., & Fennema‐Notestine, C. (2008) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2008.08.013 · Cited by 158 · see citing works
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Volumetric MRI and cognitive measures in Alzheimer disease
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Amyloid-β in Alzheimer Disease: The Null versus the Alternate Hypotheses
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Prospective motion correction of high-resolution magnetic resonance imaging data in children
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Two-year outcome of MCI subtypes and aetiologies in the Goteborg MCI study
Nordlund, A., Rolstad, S., KLANG, O., Edman, Å., Hansen, S., & Wallin, A. (2009) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2008.171066 · Cited by 117 · see citing works
Fully-Automated Volumetric MRI with Normative Ranges: Translation to Clinical Practice
Brewer, J. B. (2009) · Behavioural Neurology · doi:10.1155/2009/616581 · Cited by 114 · see citing works
Novel targets for Alzheimer's disease treatment
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Relationship between regional atrophy rates and cognitive decline in mild cognitive impairment
McDonald, C. R., Gharapetian, L., McEvoy, L. K., Fennema‐Notestine, C., Hagler, D. J., Holland, D., & Dale, A. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.03.015 · Cited by 111 · see citing works
Automated MRI measures predict progression to Alzheimer's disease
Desikan, R. S., Cabral, H., Settecase, F., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., Schmansky, N. J., Salat, D. H., & Fischl, B. R. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.023 · Cited by 109 · see citing works
Brain Atrophy in Healthy Aging Is Related to CSF Levels of Aβ1-42
Fjell, A. M., Walhovd, K. B., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Blennow, K., Brewer, J. B., Dale, A. M., & Initiative, T. A. D. N. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp279 · Cited by 105 · see citing works
Early detection of Alzheimer's disease: new diagnostic criteria
Dubois, B., Picard, G., & Sarazin, M. (2009) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2009.11.2/bdubois · Cited by 102 · see citing works
Langbaum, J. B. S., Chen, K., Caselli, R. J., Lee, W., Reschke, C., Bandy, D., Alexander, G. E., Burns, C. M., Kaszniak, A. W., Reeder, S. A., et al. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.30 · Cited by 100 · see citing works
Fully-automated volumetric MRI with normative ranges: translation to clinical practice.
Brewer, J. (2009) · PubMed · doi:10.3233/ben-2009-0226 · Cited by 98 · see citing works
Self-citationKrueger, C. E., Dean, D. L., Rosen, H. J., Halabi, C., Weiner, M. W., Miller, B. L., & Kramer, J. H. (2010) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3181a6f101 · Cited by 96 · see citing works
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Japanese Alzheimer's Disease Neuroimaging Initiative: Present status and future
Iwatsubo, T. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.011 · Cited by 94 · see citing works
Atrophy rates of the cingulate gyrus and hippocampus in AD and FTLD
Barnes, J., Godbolt, A. K., Frost, C. D., Boyes, R. G., Jones, B. F., Scahill, R. I., Rossor, M. N., & Fox, N. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.11.012 · Cited by 81 · see citing works
REVIEW: γ‐Secretase Inhibitors for the Treatment of Alzheimer's Disease: The Current State
Panza, F., Frisardi, V., Imbimbo, B. P., Capurso, C. A., Logroscino, G., Sancarlo, D., Seripa, D., Vendemiale, G., Pilotto, A., & Solfrizzi, V. (2010) · CNS Neuroscience & Therapeutics · doi:10.1111/j.1755-5949.2010.00164.x · Cited by 80 · see citing works
Ellis, K., Rowe, C., Villemagne, V., Martins, R., Masters, C., Salvado, O., Szoeke, C., & Ames, D. (2010) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2010.03.009 · Cited by 76 · see citing works
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 73 · see citing works
Toward Structural Elucidation of the γ-Secretase Complex
Li, H., Wolfe, M. S., & Selkoe, D. J. (2009) · Structure · doi:10.1016/j.str.2009.01.007 · Cited by 63 · see citing works
アルツハイマー病の再検討:アミロイド-ベータタンパク質前駆体およびアミロイド-ベータの保護的役割の証拠
Ma, S., Perry, G., Xw, Z., Nakamura, M., Hayashi, T., Nunomura, A., Sl, S., Hg, L., & Castellani, R. (2009) · Journal of Alzheimers Disease · Cited by 61 · see citing works
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 56 · see citing works
Structural neuroimaging in the detection and prognosis of pre-clinical and early AD.
Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., et al. (2009) · PubMed · doi:10.3233/ben-2009-0230 · Cited by 47 · see citing works
Self-citationCombining short interval MRI in Alzheimer’s disease
Schott, J. M., Frost, C. D., Whitwell, J. L., MacManus, D. G., Boyes, R. G., Rossor, M. N., & Fox, N. C. (2006) · Journal of Neurology · doi:10.1007/s00415-006-0173-4 · Cited by 46 · see citing works
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 · see citing works
Early risk assessment for Alzheimer's disease
Carrillo, M. C., Blackwell, A. D., Hampel, H. J., Lindborg, J., Sperling, R. A., Schenk, D., Sevigny, J., Ferris, S. H., Bennett, D. A., Craft, S., et al. (2009) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2009.01.019 · Cited by 42 · see citing works
Saumier, D., Aisen, P. S. S., Gauthier, S. G., Vellas, B. J., Ferris, S. H., Duong, A., Suhy, J., Oh, J., Lau, W. C., Garceau, D., et al. (2009) · The journal of nutrition health & aging · doi:10.1007/s12603-009-0047-4 · Cited by 34 · see citing works
Alzheimer's Disease Neuroimaging Initiative in Europe
Frisoni, G. B. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.005 · Cited by 27 · cited 2× · see citing works
“51 Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol. 2(2), 79–88 (2003).”
BackgroundLongitudinal imaging in dementia
Scahill, R. I., & Fox, N. C. (2007) · British Journal of Radiology · doi:10.1259/bjr/78981552 · Cited by 24 · see citing works
Cognitive Phenotypes, Brain Morphometry and the Detection of Cognitive Decline in Preclinical AD
Jacobson, M. W., McEvoy, L. K., Dale, A. M., & Fennema‐Notestine, C. (2009) · Behavioural Neurology · doi:10.1155/2009/617312 · Cited by 12 · see citing works
Cognitive phenotypes, brain morphometry and the detection of cognitive decline in preclinical AD.
Jacobson, M. W., McEvoy, L. K., Dale, A. M., et al. (2009) · PubMed · doi:10.3233/ben-2009-0229 · Cited by 11 · see citing works
Self-citationAisen, P. S. S. (2009) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2009.01.005 · Cited by 3 · see citing works
P1‐427: Structural neuroimaging for individual patient risk assessment in mild cognitive impairment
McEvoy, L. K., Holland, D., Hagler, D. J., Fennema‐Notestine, C., & Dale, A. M. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.05.982 · Cited by 1 · see citing works
Barbero (2014) · doi:10.1016/j · Cited by 1 · see citing works
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The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives
Swerdlow, R. H., Burns, J. M., & Khan, S. M. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2013.09.010 · Cited by 786 · see citing works
Liss, J. L., Seleri Assunção, S., Cummings, J., Atri, A., Geldmacher, D. S., Candela, S. F., Devanand, D. P., Fillit, H. M., Susman, J., Mintzer, J., et al. (2021) · Journal of Internal Medicine · doi:10.1111/joim.13244 · Cited by 312 · see citing works
Predicting MCI outcome with clinically available MRI and CSF biomarkers
Heister, D. S., Brewer, J. B., Magda, S., Blennow, K., McEvoy, L. K., & Initiative, A. D. N. (2011) · Neurology · doi:10.1212/wnl.0b013e3182343314 · Cited by 201 · see citing works
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 · see citing works
“In addition to measuring group differences in subjects with MCI or AD compared with HC ones, shape-based analysis may also help identifying MCI individuals who are suffering from prodromal AD [McEvoy and Brewer, 2010; Morris and Cummings, 2005].”
MethodByun, M. S., Yi, D., Lee, J. H., Choe, Y. M., Sohn, B. K., Lee, J., Choi, H. J., Baek, H., Kim, Y. K., Lee, Y., et al. (2017) · Psychiatry Investigation · doi:10.4306/pi.2017.14.6.851 · Cited by 116 · see citing works
Identifying incipient dementia individuals using machine learning and amyloid imaging
Mathotaarachchi, S., Pascoal, T. A., Shin, M., Benedet, A. L., Kang, M. S., Beaudry, T., Fonov, V. S., Gauthier, S., & Rosa-Neto, P. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.06.027 · Cited by 118 · see citing works
“This has been encouraged by the identification of biomarkers to detect the pathological signatures at the early stages of the dis ase (Buerger et al., 2006; Fagan et al., 2006; McEvoy and Brewer, 2010; Morris et al., 2009).”
McEvoy, L. K., Holland, D., Hagler, D. J., Fennema‐Notestine, C., Brewer, J. B., & Dale, A. M. (2011) · Radiology · doi:10.1148/radiol.11101975 · Cited by 107 · see citing works
Temporal Lobe Epilepsy: Quantitative MR Volumetry in Detection of Hippocampal Atrophy
Farid, N., Girard, H. M., Kemmotsu, N., Smith, M. E., Magda, S., Lim, W. Y., Lee, R. R., & McDonald, C. R. (2012) · Radiology · doi:10.1148/radiol.12112638 · Cited by 106 · see citing works
“Images were processed by using fully automated volumetric segmentation with a quantitative MR imaging software package (NeuroQuant; CorTechs Laboratories, La Jolla, Calif), as previously described (6,8,9).”
MethodRetinal ganglion cell dendritic degeneration in a mouse model of Alzheimer's disease
Williams, P. A., Thirgood, R. A., Oliphant, H., Frizzati, A., Littlewood, E., Votruba, M., Good, M. A., Williams, J. H., & Morgan, J. E. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.01.006 · Cited by 104 · see citing works
“In the search for an optimal set of biomarkers, central nervous system imaging has shown the most promise (McEvoy and Brewer, 2010) and significant progress has been made in the use of magnetic resonance imaging in combination with ligands that can highlight areas of central nervous system pathology (Rowe et al.”
BackgroundComparison of Automated Brain Volume Measures obtained with NeuroQuant® and FreeSurfer
Ochs, A. L., Ross, D. E., Zannoni, M. D., Abildskov, T. J., & Bigler, E. D. (2015) · Journal of Neuroimaging · doi:10.1111/jon.12229 · Cited by 88 · see citing works
Fleszar, M. G., Wiśniewski, J., Zboch, M., Diakowska, D., Gamian, A., & Krzystek-Korpacka, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-50205-0 · Cited by 80 · see citing works
Pemberton, H., Zaki, L. A. M., Goodkin, O., Das, R., Steketee, R. M. E., Barkhof, F., & Vernooij, M. W. (2021) · Neuroradiology · doi:10.1007/s00234-021-02746-3 · Cited by 80 · see citing works
“Using structured and validated QReports could provide considerable improvements in diagnostic accuracy, reliability, confidence and efficiency across a neuroradiological service but is predicated upon technical and clinical validation [2, 8, 21, 107, 108].”
BackgroundMethodUnveiling the future: Advancements in MRI imaging for neurodegenerative disorders
Du, L., Roy, S., Wang, P., Li, Z., Qiu, X., Zhang, Y., Yuan, J., & Guo, B. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102230 · Cited by 80 · see citing works
Enrichment and Stratification for Predementia Alzheimer Disease Clinical Trials
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 70 · see citing works
Bisenius, S., Mueller, K., Diehl-Schmid, J., Fassbender, K., Grimmer, T., Jessen, F., Kassubek, J., Kornhuber, J., Landwehrmeyer, B., Ludolph, A., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.02.003 · Cited by 51 · see citing works
“…changes in atrophy as visualized byMRI are an especially good biomarker for correct early diagnosis and furthermore even predictive for individuals with mild cognitive impairment to decline into AD (e.g., Frisoni et al., 2010; McEvoy and Brewer, 2010; Schroeter et al., 2009;Weiner et al., 2010).”
BackgroundMethodThe quantitative neuroradiology initiative framework: application to dementia
Goodkin, O., Pemberton, H., Vos, S. B., Prados, F., Sudre, C. H., Moggridge, J. C., Cardoso, M. J., Sebastien Ourselin, A., Bisdas, S., White, M. J., et al. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190365 · Cited by 49 · see citing works
Zainaghi, I. A., Talib, L. L., Diniz, B. S., Gattaz, W. F., & Forlenza, O. V. (2012) · Journal of Neural Transmission · doi:10.1007/s00702-012-0807-x · Cited by 46 · see citing works
“Currently, structural and functional neuroimaging techniques and cerebrospinal fluid biomarkers (amyloidb42 and Tau protein) are the most widely studied (McEvoy and Brewer 2010).”
Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer’s Disease
Rani, S., Dhar, S. B., Khajuria, A., Gupta, D., Jaiswal, P. K., Singla, N., Kaur, M., Singh, G., & Barnwal, R. P. (2023) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-023-01330-y · Cited by 48 · see citing works
“Structural MRI is a valuable predictor for observing the change of MCI to AD by measuring the degree of atrophy and memory-related structures in regions, like the hippocampus and entorhinal cortex (McEvoy and Brewer 2010).”
BackgroundOishi, K., Akhter, K., Mielke, M. M., Ceritoglu, C., Zhang, J., Jiang, H., Li, X., Laurent Younes, B., Miller, M. I., van Zijl, P. C. M., et al. (2011) · Frontiers in Neurology · doi:10.3389/fneur.2011.00054 · Cited by 44 · see citing works
“A wide array of studies have characterized the structural MRI features (for review, McEvoy and Brewer, 2010) and the diffusion tensor imaging (DTI) features (for review, Chua et al., 2008; Stebbins and Murphy, 2009), related to AD.”
BackgroundPemberton, H., Goodkin, O., Prados, F., Das, R., Vos, S. B., Moggridge, J. C., Coath, W., Gordón, E., Barrett, R., Schmitt, A., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07455-8 · Cited by 42 · see citing works
Ma, D., Holmes, H. E., Cardoso, M. J., Modat, M., Harrison, I. F., Powell, N. M., O’Callaghan, J. M., Ismail, O., Johnson, R. A., O’Neill, M. J., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00011 · Cited by 36 · see citing works
“In neuroimaging studies, quantitative analysis of neuroanatomy, such as volumetric analysis of brain structures extracted from magnetic resonance imaging (MRI) data sets, plays a crucial role in the diagnosis of diseases at the early stages of pathology before the onset of clinical symptoms (McEvoy and Brewer, 2010).”
BackgroundDementia prediction with multimodal clinical and imaging data
Kubi, N. N. B. A., & Nazir, S. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02326-7 · Cited by 29 · see citing works
“Structural MRI is highly regarded for its precision in the early detection of Alzheimer’s disease-related dementia, particularly in persons expe - riencing moderate cognitive impairment [14].”
Imaging as a biomarker in drug discovery for Alzheimer’s disease: is MRI a suitable technology?
Pich, E. M., Jeromin, A., Frisoni, G. B., Hill, D., Lockhart, A., Schmidt, M. E., Turner, M. R., Mondello, S., & Potter, W. Z. (2014) · Alzheimer s Research & Therapy · doi:10.1186/alzrt276 · Cited by 29 · see citing works
“proved as a medical device to provide an aid in dementia diagnosis [18].”
BackgroundMethodOdimayo, S., Olisah, C. C., & Mohammed, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-60611-8 · Cited by 26 · see citing works
“• We investigate SNeurodCNN sensitives to brain neurodegeneration for perceiving important digital biomarkers (digi-biomarkers) in order to identify facets of the brain where focal structural neurodegeneration of the cerebral cortex is prevalent.”
BackgroundAn Expanded Role for Neuroimaging in the Evaluation of Memory Impairment
Desikan, R. S., Rafii, M. S., Brewer, J. B., & Hess, C. P. (2013) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3644 · Cited by 26 · see citing works
“can detect AD-associated volume loss, predict disease progression, and be used as an outcome measure in therapeutic trials.(26,27) Recently, the FDA has approved one such vMRI technology(28) that”
He, C., Ahmed, T., Abbasi, A. Z., Li, L. Y., Foltz, W. D., Cai, P., Knock, E., Fraser, P. E., Rauth, A. M., Henderson, J. T., et al. (2020) · Nano Today · doi:10.1016/j.nantod.2020.100965 · Cited by 24 · see citing works
Rogne, S., Vangberg, T. R., Eldevik, P., Wikran, G., Mathiesen, E. B., & Schirmer, H. (2016) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000450885 · Cited by 23 · see citing works
Kotloski, R. J., Dowding, J., Hermann, B. P., & Sutula, T. P. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804766-8.00025-x · Cited by 23 · see citing works
Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes
Brinkmann, B. H., Guragain, H., Kenney‐Jung, D. L., Mandrekar, J. N., Watson, R. E., Welker, K. M., Britton, J. W., & Witte, R. J. (2019) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.50885 · Cited by 21 · see citing works
“Detection of mild hippocampal atrophy requires accurate and reproducible quantitative measurement of hippocampal volumes as well as normative measures.(9)”
MethodImaging of neurodegenerative cognitive and behavioral disorders
Atri, A. (2016) · Handbook of clinical neurology · doi:10.1016/b978-0-444-53486-6.00050-8 · Cited by 19 · see citing works
“Yet, visual quality review of the results by an imaging expert remains important, and the imaging expert, as well as the referring physician, retains the ability to scroll through segmented three-dimensional MRI images to determine accuracy of the segmentation (McEvoy and Brewer, 2010).”
BackgroundEmergence of breath testing as a new non-invasive diagnostic modality for neurodegenerative diseases
Subramaniam, N. S., Bawden, C. S., Waldvogel, H. J., Faull, R. L. M., Howarth, G. S., & Snell, R. G. (2018) · Brain Research · doi:10.1016/j.brainres.2018.04.017 · Cited by 17 · see citing works
Quantification of perforant path fibers for early detection of Alzheimer's disease
Uchida, Y., Hou, Z., Gómez-Isaza, L., Luongo, M., Troncoso, J. C., Miller, M. I., Mori, S., & Oishi, K. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70142 · Cited by 16 · see citing works
Bulboacă, A. E., Bolboaca, S. D., Bulboacă, A., & Prodan, C. I. (2016) · Archives of Medical Science · doi:10.5114/aoms.2016.60129 · Cited by 15 · see citing works
“Evidence of structural changes may be found even in patients with mild cognitive impairment [13].”
BackgroundA systematic review of (semi-)automatic quality control of T1-weighted MRI scans
Hendriks, J., Mutsaerts, H. J. M. M., Joules, R., Peña‐Nogales, Ó., Rodrigues, P., Wolz, R., Burchell, G. L., Barkhof, F., & Schrantee, A. (2023) · Neuroradiology · doi:10.1007/s00234-023-03256-0 · Cited by 15 · see citing works
“, 2013), disease (McEvoy and Brewer, 2010), and the effects of disease-modifying therapies (Paolillo et al.”
BackgroundBiomarkers for the clinical evaluation of the cognitively impaired elderly: amyloid is not enough
McEvoy, L. K., & Brewer, J. B. (2012) · Imaging in Medicine · doi:10.2217/iim.12.27 · Cited by 15 · see citing works
Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer's Disease
Trzepacz, P. T., Hochstetler, H. M., Yu, P., Castelluccio, P., Witte, M. M., Dell’Agnello, G., Degenhardt, E. K., & Initiative, A. D. N. (2015) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000441351 · Cited by 15 · see citing works
Fung, Y. L., Ng, K. E. T., Vogrin, S. J., Meade, C. E., Ngo, M. M., Collins, S. J., & Bowden, S. C. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181172 · Cited by 15 · see citing works
Structural Neuroimaging of the Medial Temporal Lobe in Alzheimer’s Disease Clinical Trials
Menéndez‐González, M., Alonso, B. D. C., Salas-Pacheco, J. M., & Arias-Carrión, Ó. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150226 · Cited by 14 · see citing works
“While harmonization of manual hippocampal segmentation is in progress [12, 32, 36, 37, 44], automated image analysis software was recently approved as a medical device to aid dementia diagnosis [45].”
Quantitative MRI Differences Between Early versus Late Onset Alzheimer’s Disease
Meysami, S., Raji, C. A., Merrill, D. A., Porter, V. R., & Mendez, M. F. (2021) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/15333175211055325 · Cited by 13 · see citing works
“Additionally, abnormal brain MRI volume loss can be noted 3–5 years prior to onset of memory loss symptoms.(34) Thus, with quantification as demonstrated in this work, there remains potential to extract Figure 2.”
BackgroundNasra, P., & Gupta, S. (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940932 · Cited by 13 · see citing works
“Therefore, creating predictive diagnosis models and tools grounded on deep learning and machine learning techniques is needed to increase the accuracy and speed of MRI scan-based differences in Alzheimer's disease [4].”
Sensitivity and specificity of executive function tests for Alzheimer’s disease
Huang, S., Liu, C., Chang, C., & Su, C. (2016) · Applied Neuropsychology Adult · doi:10.1080/23279095.2016.1204301 · Cited by 13 · see citing works
“…lobe degeneration and a disconnection between anterior (frontal) and posterior (temporal and parietal) association areas are implicated as the cause of the executive impairment in the early stages of AD (Collette et al., 1999; McEvoy & Brewer, 2010; Scahill, Schott, Stevens, Rossor, & Fox, 2002).”
BackgroundNigro, S. L., Filardi, M., Tafuri, B., Nicolardi, M., Blasi, R. D., Giugno, A., Gnoni, V., Milella, G., Urso, D., Zoccolella, S., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230151 · Cited by 12 · see citing works
Ross, D. E., Graham, T. J., & Ochs, A. L. (2012) · Psychological Injury and Law · doi:10.1007/s12207-012-9140-9 · Cited by 12 · see citing works
Ross, D. E., Seabaugh, J. D., Seabaugh, J. M., Barcelona, J., Seabaugh, D., Wright, K., Norwind, L., King, Z., Graham, T. J., Baker, J. R., et al. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.715807 · Cited by 11 · see citing works
Kukreja, L., Shahidehpour, R. K., Kım, G., Keegan, J., Sadleir, K. R., Russell, T. A., Csernansky, J. G., Mesulam, M. M., Vassar, R. J., Wang, L., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1836-17.2018 · Cited by 11 · see citing works
“Structural MRI is used extensively in clinical settings to detect patterns of atrophy characteristic of different neurodegenerative disorders (Frisoni et al., 2010; McEvoy and Brewer, 2010).”
BackgroundOverview of MR Imaging Volumetric Quantification in Neurocognitive Disorders
Raji, C. A., Ly, M. J., & Benzinger, T. L. S. (2019) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000224 · Cited by 10 · see citing works
Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning ApproachPreprint
Orozco-Sanchez, J., Treviño, V., Martínez-Ledesma, E., Farber, J., & Tamez‐Peña, J. G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/836510 · Cited by 10 · see citing works
“Image-based findings associated with a disease process are called imaging-biomarkers [13], [14].”
BackgroundTsiara, A. A., Plakias, S., Kokkotis, C., Veneri, A., Mina, M. A., Tsiakiri, A., Kitmeridou, S., Christidi, F., Gourgoulis, E., Doskas, T., et al. (2025) · Neurology International · doi:10.3390/neurolint17030045 · Cited by 20 · see citing works
“Traditional tools such as electroencephalograms (EEGs), magnetic resonance imaging (MRI), and computed tomography (CT) scans provide valuable insights, but often fall short in precision and scalability, particularly for early detection [5–9].”
Ghiyamihoor, F., Peymani, P., Perron, J., Asemi‐Rad, A., Marzban, M., Mohite, A., Ardila, K., Aljada, B., Marzban, A., Toback, M., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70214 · Cited by 9 · see citing works
“…aging (Whitwell et al. 2007); (2) enhances understanding of disease neuropathology, potentially aiding their early detection and design of targeted interventions (Glodzik-Sobanska et al. 2005; McEvoy and Brewer 2010); and (3) reveals both sex-specific and general mechanisms of vulnerability.”
Multimodal magnetic resonance imaging analysis of early mild cognitive impairment
Xu, S., Fan, Y., Mao, C., Hu, Z., Yang, Z., Qu, L., Xu, Y., Yu, L., Zhu, X., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251321187 · Cited by 8 · see citing works
“20 Since brain atrophy is apparent in individuals with MCI before the dementia symptoms, 21 identifying volume reduction in characteristic brain areas such as the hippocampus and amygdala in the early stage may contribute to the diagnosis of MCI.”
Quek, Y., Fung, Y. L., Cheung, M. W. ‐., Vogrin, S. J., Collins, S. J., & Bowden, S. C. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28037 · Cited by 8 · see citing works
“Taken together, these findings suggest that MRI-based regional brain volume measurements could play a valuable role in supporting early AD detection and confirmation in the clinical setting.(35) The current gold standard method to measuring regional brain volumes on structural MRI is manual segmentation.”
BackgroundZhang, H., Zhu, F., Dodge, H. H., Higgins, G. A., Omenn, G. S., Guan, Y., & Initiative, T. A. D. N. (2018) · GigaScience · doi:10.1093/gigascience/giy085 · Cited by 7 · see citing works
Zamani, J., Sadr, A., & Javadi, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.18.256321 · Cited by 7 · see citing works
Sönmez, T. F., Harvey, D. J., Beckett, L. A., & Initiative, A. D. N. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14524 · Cited by 6 · see citing works
Li, L. Y., Park, E., He, C., Abbasi, A. Z., Henderson, J. T., Fraser, P. E., Uetrecht, J., Rauth, A. M., & Wu, X. Y. (2024) · Nanotoxicology · doi:10.1080/17435390.2024.2361687 · Cited by 5 · see citing works
“…and local glucose metabolism, and magnetic resonance imaging (MRI) that measures anatomical and functional changes in the brain (Mistur et al. 2009; Mcevoy and Brewer 2010; Verclytte et al. 2016; Ortner et al. 2019). however, most of the detectable aD-associated changes only appear in the late…”
The Role of Neuroimaging in the Assessment of the Cognitively Impaired Elderly.
Kollack-Walker, S., Liu, C. Y., & Fleisher, A. (2017) · Neurologic clinics · doi:10.1016/j.ncl.2017.01.010 · Cited by 5 · see citing works
Li, F., Cheng, J., Jin, K., Zhao, L., Li, J., Wu, J., & Ren, X. (2024) · Brain and Behavior · doi:10.1002/brb3.70111 · Cited by 4 · see citing works
“MRI, on the other hand, is more accessible and cost-effective, focusing on brain structure changes (McEvoy and Brewer 2010).”
BackgroundMao, C., Hou, B., Li, J., Chu, S., Huang, X., Wang, J., Dong, L., Liu, C., Feng, F., Peng, B., et al. (2022) · Current Alzheimer Research · doi:10.2174/1567205019666220905145756 · Cited by 4 · see citing works
Projecting MRI brain images for the detection of Alzheimer's Disease
Martínez-Murcia, F. J., Manuel, G. J., RamÃrez, J., Ignacio, &. I., Salas-Gonzalez, D., Segovia, F., & Initiative, A. D. N. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-474-9-225 · Cited by 4 · see citing works
“Numerous works have explored the capacity of MRI for detecting and predicting AD using several parameters, as the cortical thickness [2], analysis of Regions of Interest (ROIs) [7] or compared measures of brain region atrophy [6].”
Fast Brain Volumetric Segmentation from T1 MRI Scans
Anand, A., & Anand, N. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-17795-9_30 · Cited by 3 · see citing works
Early Detection of Alzheimer's disease by using the Latest TechniquesPreprint
Ahmad, S., Aijaz, M., Ahmad, M., Hafeez, A., Asif, M., Kumar, A., Al‐Taie, A., & Ansari, M. A. (2023) · doi:10.22541/essoar.168056798.85734608/v1 · Cited by 3 · see citing works
MR Brain Screening using Optimization Techniques - A Survey
Chitradevi, D., & Prabha, S. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666211126154101 · Cited by 2 · see citing works
Impact of Chronic Conditions and Dementia in Rural West Texas: A Healthy Aging Study
Morton, H., Basu, T., Bose, C., & Reddy, P. H. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220084 · Cited by 2 · see citing works
Зуева, М. В., Zhuravleva, A. N., & Bogolepova, A. N. (2021) · Ophthalmology in Russia · doi:10.18008/1816-5095-2021-2-198-207 · Cited by 2 · see citing works
Mozafar, M., Shahbazi, S., Amirian, M. A., Shekari, K., Sepahvand, N., Esmaeili, M., Amini, A., Shakeri, S., Mirhosseini, H., Mayeli, M., et al. (2025) · Applied Neuropsychology Adult · doi:10.1080/23279095.2025.2546951 · Cited by 1 · see citing works
Buchert, R., Suppa, P., Ardekani, B. A., Bataller, F. B., Bourgeat, P., Pierrick Coupé, A., Dahnke, R., Devenyi, G. A., Eskildsen, S. F., Fischer, C., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251380301 · Cited by 1 · see citing works
Motion-Informed 3D Deep Learning Reconstruction in Patients with Cognitive Impairment
Fujita, S., Polak, D., Nickel, D. M., Splitthoff, D. N., Huang, Y., Gil, N., Buathong, S., Chiang, C.-H., Lo, W., Clifford, B., et al. (2025) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8977 · Cited by 1 · see citing works
Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
Ilias, L., Vozinaki, A.-M., Ntanos, C., & Askounis, D. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357133 · Cited by 1 · see citing works
“Magnetic Resonance Imaging (MRI) complements PET by identifying structural changes, such as atrophy or shrinkage in critical areas, which are often affected in the early stages of the disease [4].”
Зуева, М. В., Zhuravleva, A. N., & Bogolepova, A. N. (2022) · Ophthalmology in Russia · doi:10.18008/1816-5095-2022-3-532-540 · Cited by 1 · see citing works
Contemporary Trends in MRI and PET Imaging for the Early Diagnosis of Alzheimer’s Disease
Chan, B. (2022) · International journal of high school research · doi:10.36838/v4i6.2 · Cited by 1 · see citing works
COMPUTERIZED TOOLS: A SUBSTITUTE OR A SUPPLEMENT WHEN DIAGNOSING ALZHEIMER'S DISEASE?
Palomeque, A., & Alberto, C. (2015) · Figshare
Diagnosis of Alzheimer’s Disease Using Brain Imaging: State of the Art
Shah, A., Niaz, K., Ahmed, M., & Bunyan, R. F. (2019) · Springer eBooks · doi:10.1007/978-981-13-9636-6_7
An empirical study of digital signal filtering to improve Alzheimer’s disease detection
Gonzales, C., & Rodriguez, B. M. (2022) · SPIE Applications of Machine Learning · doi:10.1117/12.2643563
“In this study, the (fpass, fstop) pairs are used to create the high-pass Butterworth filter are: (16, 8), (12, 8), and (8, 4).”
MethodA Survey on Optimization Methods Used for Early Prediction and Diagnosis of Schizophrenia Disorder
Swathi, N., & Prabha, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-42685-8_15
Gurmendi, D. S. M. R., Fumagal-González, G. A., Orozco-Sanchez, J., Farber, J., Treviño, V., Martínez-Ledesma, E., Martínez‐Torteya, A., Rosales-Gurmendi, F., Tamez‐Peña, J. G., & Initiative, A. D. N. (2025) · medRxiv · doi:10.1101/2025.03.11.25323801
“These imaging modalities can visualize direct changes in brain structure, function, sugar metabolism, and AD build-up [20, 21].”
Structural Neuroimaging Markers of Dementia: Insights from ROC Curve AnalysisPreprint
Pepper, S., Dzesu, S., Yoksh, L., Patel, A., Honea, R. A., & Mudaranthakam, D. P. (2025) · Research Square · doi:10.21203/rs.3.rs-7418658/v1
“Current clinical practice often relies on qualitative interpretation or subjective thresholds, which may vary across institutions and practitioners (McEvoy & Brewer, 2010).”
Map the image: Machine learning for quantitative MR mapping
Feng, L., Pei, H., & Liu, F. (2025) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-443-14109-6.00010-9
Enhanced Hybrid Deep Learning Model for Alzheimer Disease Diagnosis
N, B. M. T., Nayana, B. R., & Deepak, S. (2025) · International Conference on Communication, Computer, and Information Technology (IC3IT) · doi:10.1109/ic3it66137.2025.11340957
“Although machine learning algorithms have been applied to diagnose Alzheimer's disease using MRI data, different approaches often yield varying results [9].”
Learning Based Diagnosis of Alzheimer's Disease Using Genomic Data
Jayasingh, B. B., & Mallick, S. (2025) · International Conference on Networks and Soft Computing (ICNSoC) · doi:10.1109/icnsoc66817.2025.00102
Automatic segmentation and shape analysis of human hippocampus in Alzheimer's disease
Shen, K. (2011) · doi:10.70675/44a642b1z9128z4596z92f8z0f9767a16911
“Clinical and research of structural MRI imaging marker in AD are recently reviewed by Frisoni et al. (2010) and McEvoy and Brewer (2010).”
Li, I. D., Ung, C. Y., & Correia, C. (2026) · Journal of Imaging · doi:10.3390/jimaging12080352
“This makes early classification from MRI alone difficult, especially in individuals at the boundary between cognitively normal aging and mild impairment [4,5].”
AI-Driven Diagnosis of Neurological Disorders: A Comparative Study of ML and DL Techniques
Berradi, M., Malali, H. E., Youssef, Y. B., Taleb, L. B., Indjaren, H., & Mouhsen, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-11411-2_15
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“Additionally, volumetric or cortical-thickness measures derived from single regions may overlook the broader, multi-region atrophy patterns that characterize AD, thereby limiting sensitivity to early or spatially distributed structural changes [6].”
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“Structural magnetic resonance imaging helps visualize atrophic pattern of the brain and enables non-invasive evaluation of the disease progression through anatomical biomarkers of neurodegeneration [3][4].”
Unschuld, P. G. (2026) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-443-21425-7.00014-2
- Authors
- McEvoy, L. K., & Brewer, J. B.
- Affiliations
- University of California San Diego (US)
- University of California System (US)
- ORCID
- McEvoy, Linda K
- Venue
- Expert Review of Neurotherapeutics
- Publisher
- Informa UK Limited
- Published
- 2010-11
- Volume
- 10
- Issue
- 11
- Pages
- 1675-1688
- ISSN
- 1473-7175 (ISSN-L)
- 1744-8360
- PMC
- PMC3182103
- OpenAlex
- W2002277005
- Cited by
- 90
- References
- 138
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 6 · 2023: 6 · 2022: 6 · 2021: 7 · 2020: 2 · 2019: 10 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 3 · 2012: 5
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Expert Review of Neurotherapeutics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (K01AG029218)
- National Institute of Neurological Disorders and Stroke (K02NS067427)
- 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 › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, early detection, preclinical Alzheimer's disease, mild cognitive impairment, brain atrophy, neurodegeneration, disease-modifying therapy, clinical translation
- MeSH terms
- Alzheimer Disease, Brain, Cognition Disorders, Humans, Magnetic Resonance Imaging, Disease Progression, Early Diagnosis
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