Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort
Open accessAbstract
Cross sectional studies of patients at risk of developing Alzheimer disease (AD) have identified several brain regions known to be prone to degeneration suitable as biomarkers, including hippocampal, ventricular, and whole brain volume. The aim of this study was to longitudinally evaluate an index based on morphometric measures derived from MRI data that could be used for classification of AD and healthy control subjects, as well as prediction of conversion from mild cognitive impairment (MCI) to AD. Patients originated from the AddNeuroMed project at baseline (119 AD, 119 MCI, 110 controls (CTL)) and 1-year follow-up (62 AD, 73 MCI, 79 CTL). Data consisted of 3D T1-weighted MR images, demographics, MMSE, ADAS-Cog, CERAD and CDR scores, and APOE e4 status. We computed an index using a multivariate classification model (AD vs. CTL), using orthogonal partial least squares to latent structures (OPLS). Sensitivity, specificity and AUC were determined. Performance of the classifier (AD vs. CTL) was high at baseline (10-fold cross-validation, 84% sensitivity, 91% specificity, 0.93 AUC) and at 1-year follow-up (92% sensitivity, 74% specificity, 0.93 AUC). Predictions of conversion from MCI to AD were good at baseline (77% of MCI converters) and at follow-up (91% of MCI converters). MCI carriers of the APOE e4 allele manifested more atrophy and presented a faster cognitive decline when compared to non-carriers. The derived index demonstrated a steady increase in atrophy over time, yielding higher accuracy in prediction at the time of clinical conversion. Neuropsychological tests appeared less sensitive to changes over time. However, taking the average of the two time points yielded better correlation between the index and cognitive scores as opposed to using cross-sectional data only. Thus, multivariate classification seemed to detect patterns of AD changes before conversion from MCI to AD and including longitudinal information is of great importance.
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Self-citationAPOE ε2 Allele Is Associated with Larger Regional Cortical Thicknesses and Volumes
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Self-citationClark, V. H., Resnick, S. M., Doshi, J. J., Beason‐Held, L. L., Zhou, Y., Ferrucci, L., Wong, D. F., Kraut, M. A., & Davatzikos, C. A. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.01.010 · Cited by 38 · see citing works
Combining MRI and MRS to Distinguish Between Alzheimer's Disease and Healthy Controls
Westman, E., Wahlund, L., Foy, C., Poppe, M., Cooper, A., Murphy, D., Spenger, C., Lovestone, S., & Simmons, A. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100168 · Cited by 33 · see citing works
Self-citationThe genetics of episodic memory
Koppel, J. L., & Goldberg, T. E. (2009) · Cognitive Neuropsychiatry · doi:10.1080/13546800902990438 · Cited by 22 · cited 1× · see citing works
“, 2013), maybe because precise measures of cognitive decline are particularly difficult at this stage, and also because of individual variation due to cognitive reserve and genetic background (Small et al., 2004; Koppel and Goldberg, 2009; Wisdom et al., 2011; Bogdan et al., 2012).”
BackgroundAPOE Genotype in the Diagnosis of Alzheimer’s Disease in Patients With Cognitive Impairment
Sun, X., Nicholas, J. S., Walker, A., Wagner, M. T., & Bachman, D. (2012) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317512452037 · Cited by 19 · see citing works
Structural imaging in early pre-states of dementia
Smith, C. D. (2011) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2011.07.002 · Cited by 11 · see citing works
(2014) · Institutional Repository University of Antioquia (University of Antioquia) · doi:10.1371/journal.pone
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
Flagged first, then by citations
Cited by
Sorted by citations
Lombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009628.pub2 · Cited by 214 · see citing works
Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images
Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. G. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2704614 · Cited by 181 · see citing works
“sults than only using one time-point data (such as cross-sectional data) [18].”
BackgroundQuantitative EEG power and synchronization correlate with Alzheimer's disease CSF biomarkers
Smailovic, U., Koenig, T., Kåreholt, I., Andersson, T., Kramberger, M. G., Winblad, B., & Jelic, V. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.11.005 · Cited by 177 · see citing works
“MRI and PET topographical markers have been shown consistently to predict the development of AD dementia in MCI cohorts (Aguilar et al., 2014; Morris et al., 2009) and to correlate with disease severity (Jack et al.”
Löwe, L. C., Christian Gaser, B., Franke, K., & Initiative, A. D. N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157514 · Cited by 147 · see citing works
“measurements in a recent study using a MRI-based index for diagnosis and prediction of conversion [57].”
MethodResultCerebral atrophy in mild cognitive impairment: A systematic review with meta‐analysis
Tabatabaei‐Jafari, H., Shaw, M. E., & Cherbuin, N. (2015) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2015.11.002 · Cited by 128 · see citing works
“Moreover, the effect appears to become more salient across the disease process with MCI and AD showing that the APOE ε4 genotype is associated with faster atrophy rates [43,44], particularly in the hippocampus [45,46].”
BackgroundLi, H., & Fan, Y. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) · doi:10.1109/isbi.2019.8759397 · Cited by 83 · see citing works
“Longitudinal changes of biomarkers and imaging markers have been investigated for AD diagnosis/prognosis, for instance, changes of neuropsychological measures [12], atrophy rate in cortical thickness and subcortical volume [9, 13-15], and changes in brain tissue intensity/density map [10].”
MethodLongitudinal analysis for Alzheimer's disease diagnosis using RNN
Cui, R., Liu, M., & Li, G. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363833 · Cited by 77 · see citing works
“[18] Aguilar C, Muehlboeck J, Mecocci P, et al.”
BackgroundResultFerreira, D., Machado, A., Molina, Y., Nieto, A., Correia, R., Westman, E., & Barroso, J. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00188 · Cited by 75 · see citing works
“…(Liu et al., 2011), imaging-cerebrospinal fluid analysis (Ferreira et al., 2014b), biomarker discovery (Thambisetty et al., 2011), and generation of a multivariate diagnostic index for AD diagnosis and SCD and MCI prediction (Westman et al., 2010; Aguilar et al., 2014; Ferreira et al., 2017a).”
BackgroundSelf-citationThe Worldwide Alzheimer's Disease Neuroimaging Initiative: An update
Hendrix, J. A., Finger, B., Weiner, M. W., Frisoni, G. B., Iwatsubo, T., Rowe, C. C., Kim, S. Y., Guinjoan, S. M., Sevlever, G. E., & Carrillo, M. C. (2015) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.05.008 · Cited by 65 · see citing works
“The classifier models involved cortical thickness measures and subcortical volumes in the first study [60] and hippocampal subfield volumes in the second study [61].”
Prediction of Alzheimer’s Disease Progression Based on Magnetic Resonance Imaging
Zhou, Y., Song, Z., Han, X., Li, H., & Tang, X. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00472 · Cited by 46 · see citing works
Falahati, F., Ferreira, D., Soininen, H. S., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Lovestone, S., Eriksdotter, M., Wahlund, L., et al. (2015) · Brain Topography · doi:10.1007/s10548-015-0455-1 · Cited by 45 · see citing works
Self-citationFerreira, D., David Bartrés-faz, A., Nygren, L., Rundkvist, L. J., Molina, Y., Machado, A., Junqué, C., Barroso, J., & Westman, E. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.05.061 · Cited by 41 · see citing works
Self-citationRoss, F. C., Mayer, D. E., Horn, J. V., Cryan, J. F., Rio, D. D., Randolph, E., Gill, C. I. R., Gupta, A., Ross, R. P., Stanton, C., et al. (2024) · Nutritional Neuroscience · doi:10.1080/1028415x.2023.2298098 · Cited by 37 · see citing works
“Multimodal brain imaging, a biomarker of structural and functional brain integrity [116] has contributed to our understanding of the neurobiology of cognitive function and disease progression in AD [115,117,118].”
Early Prediction of Alzheimer’s Disease Using Null Longitudinal Model-Based Classifiers
Gavidia‐Bovadilla, G., Kanaan-Izquierdo, S., Mataró, M., Perera-Lluna, A., & Initiative, A. D. N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0168011 · Cited by 37 · see citing works
“Other studies have proposed methods to calculate an 87 MRI-based AD severity index from cross-sectional [23] and longitudinal studies [24], 88 where multivatiate models based on OPLSR were built from HC and AD subject data.”
Orellana, C., Ferreira, D., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Lovestone, S., Simmons, A., et al. (2015) · Neurodegenerative Diseases · doi:10.1159/000442443 · Cited by 36 · see citing works
“In line with the present study, this severity index showed an association with MMSE, ADAS-Cog, CDR-SOB and ApoE ε4 status, both cross-sectionally and longitudinally [15, 20] .”
Self-citationMachado, A., Barroso, J., Molina, Y., Nieto, A., Díaz‐Flores, L., Westman, E., & Ferreira, D. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.07.017 · Cited by 36 · see citing works
Self-citationFalahati, F., Ferreira, D., Muehlboeck, J.-S., Eriksdotter, M., Simmons, A., Wahlund, L., & Westman, E. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.08.014 · Cited by 30 · see citing works
“Recently, we proposed a methodology using multivariate analysis of automated MRI-derived structural measures (global and regional thickness and volumes) to generate a disease severity index (SI) (Aguilar et al., 2014; Spulber et al., 2013).”
Self-citationTR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network
Fan, C.-C., Peng, L., Wang, T., Yang, H., Zhou, X.-H., Ni, Z.-L., Wang, G., Chen, S., Zhou, Y.-J., & Hou, Z.-G. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3151118 · Cited by 32 · see citing works
“Previous longitudinal studies [15]–[17] focused on analyzing the speed of biomarkers (e.g., hippocampus, enterorhinal cortex) over time.”
Chiarelli, A. M., Fletcher, M. A., Tan, C. H., Low, K. A., Maclin, E. L., Zimmerman, B., Kong, T. S., Gorsuch, A., Gratton, G., & Fabiani, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.064 · Cited by 30 · see citing works
“Previous research has also shown a link between local cortical atrophy and specific changes in cognitive outcomes (Aguilar et al., 2014; Draganski et al., 2013; Hedden and Gabrieli, 2004; Hedden et al., 2014; Kaup et al., 2011; Raz et al., 1998; Raz and Rodrigue, 2006; Rodrigue and Raz, 2004;…”
ResultFerreira, D., Falahati, F., Linden, C., Buckley, R. F., Ellis, K. A., Savage, G. R., Villemagne, V. L., Rowe, C. C., Ames, D. J., Simmons, A., et al. (2017) · Scientific Reports · doi:10.1038/srep44368 · Cited by 29 · see citing works
Self-citationHuang, W., Li, X., Li, H., Wang, W., Chen, K., Xu, K., Zhang, J., Chen, Y., Wei, D., Shu, N., et al. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021200171 · Cited by 24 · see citing works
“This finding suggests that the brains of APOE ́4 carriers may age faster than noncarriers, which has been supported by longitudinal studies (40).”
ResultLerch, O., Ferreira, D., Stomrud, E., van Westen, D., Tideman, P., Palmqvist, S., Mattsson, N., Hort, J., Hansson, O., & Westman, E. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01517-5 · Cited by 22 · see citing works
“We used OPLS to create a “disease severity index”, using multiple structural MRI measures as input, allowing us to predict progression from MCI to dementia [20, 21] and from SCD to MCI or dementia [22].”
BackgroundMethodSelf-citationStructural neuroimaging in neuropsychology: History and contemporary applications.
Bigler, E. D. (2017) · Neuropsychology · doi:10.1037/neu0000418 · Cited by 20 · see citing works
Coupled sleep rhythm disruption predicts cognitive decline in Alzheimer’s disease
Wei, T., Zhou, J., Wang, Z., Liu, X., Mi, Y., Zhao, Y., Xing, Y., Zhao, B., Zhou, S., Liu, Y., et al. (2025) · 中国科学通报：英文版 · doi:10.1016/j.scib.2025.03.023 · Cited by 16 · see citing works
A quarter century of advances in the statistical analysis of longitudinal neuropsychological data.
Woodard, J. L. (2017) · Neuropsychology · doi:10.1037/neu0000386 · Cited by 16 · see citing works
Lemercier, P., Vergallo, A., Lista, S., Zetterberg, H., Blennow, K., Potier, M., Habert, M., Lejeune, F., Dubois, B., Teipel, S. J., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.07.005 · Cited by 10 · see citing works
“For instance, in AD clinical research, PLS was used for diagnostic classification tasks ( Aguilar et al., 2014 ), omics studies ( Lorenzi et al., 2018 ; Vardarajan et al., 2020 ; Xicota et al., 2019 ) and early longitudinal cognitive decline ( Langbaum et al., 2020 ).”
MethodJardim, N. Y. V., Bento-Torres, N. V. O., Tomás, A. M., da Costa, V. O., Bento‐Torres, J., & Diniz, C. W. P. (2023) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2023.105206 · Cited by 8 · see citing works
Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images
Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61188-4_4 · Cited by 7 · see citing works
“analyzed the longitudinal atrophy changes in cortical thickness measures and subcortical volumes, and pointed out that the use of two time points data yielded better index result compared with using the cross-sectional data only [17].”
ResultEarly Classification of Abnormal Health using Longitudinal Structural MRI Data
Devika, K. N., & Murthy, O. V. R. (2020) · IEEE India Conference · doi:10.1109/indicon49873.2020.9342532 · Cited by 6 · see citing works
“[17] carried out longitudinal based AD classification by extracting CT and subcortical volumes from AddNeuroMed longitudinal dataset and noticed that compared to crossectional features combined features from longitudinal MRI sessions yielded better results.”
ResultNew perspectives in the search for reliable biomarkers in Alzheimer disease
Moreno‐Martínez, L., Osta, R., & Calvo, A. C. (2015) · The European Journal of Psychiatry · doi:10.4321/s0213-61632015000100005 · Cited by 3 · see citing works
Spectral Shape Analysis of the Hippocampal Structure for Alzheimer’s Disease Diagnosis
Initiative, F. T. A. D. N., Maicas, G., Muñoz, A. I., Galiano, G., Hamza, A. B., & Schiavi, E. (2016) · SEMA SIMAI Springer series · doi:10.1007/978-3-319-32013-7_2 · Cited by 2 · see citing works
Piersson, A. D., Mohamad, M., Rajab, F., & Suppiah, S. (2020) · medRxiv · doi:10.1101/2020.12.23.20248759 · Cited by 1 · see citing works
“Two studies did not account for the frequency of APOE ε4 (38,41),”
BackgroundResultInfluentialCo morbidity and Neuroimaging in Alzheimer’s Disease
Tsolaki, A. (2018) · Gerontology & Geriatrics Studies · doi:10.31031/ggs.2018.03.000567
“The volume decrease in entorhinal cortex and hippocampus areas is strongly associated with AD [78,79].”
Background
- Authors
- Aguilar, C., Muehlboeck, J.-S., Mecocci, P., Vellas, B., Tsolaki, M., Kloszewska, I., Soininen, H., Lovestone, S., Wahlund, L.-O., Simmons, A., & Westman, E.
- Affiliations
- Karolinska Institutet (SE)
- King's College London (GB)
- University of Perugia (IT)
- Inserm (FR)
- Aristotle University of Thessaloniki (GR)
- Kuopio University Hospital (FI)
- Corresponding author
- Aguilar, Carlos
- ORCID
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2014-07-14
- Volume
- 6
- Pages
- 145-145
- ISSN
- 1663-4365 (ISSN-L)
- PMC
- PMC4094911
- OpenAlex
- W2046189693
- Cited by
- 34
- References
- 86
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 3 · 2020: 3 · 2019: 1 · 2018: 3 · 2017: 10 · 2016: 3 · 2015: 5
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Aging Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- South London and Maudsley NHS Foundation Trust
- National Institute for Health and Care Research (NF-SI-0512-10053)
- European Commission (FP6-2004-LIFESCIHEALTH-5)
- Karolinska Institutet
- Stockholms Läns Landsting
- Swedish Brain Power
- Menzies Centre for Australian Studies, King's College London, University of London
- 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, mild cognitive impairment, longitudinal brain atrophy, conversion prediction, hippocampal volume, ventriculomegaly, total brain volume, APOE ε4, cognitive decline, multivariate classification, orthogonal partial least squares
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