BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI). METHODS: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

Clinical Memory Research Unit Department of Clinical Sciences Malmö Lund University Malmö 20502 Sweden

Department of Neuroimaging Centre for Neuroimaging Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London SE58AF UK

Department of Radiology Mayo Clinic Rochester MN 55902 USA

Diagnostic Radiology Institution for Clinical Sciences Lund Lund University Lund 22184 Sweden

Division of Clinical Geriatrics Centre for Alzheimer Research Department of Neurobiology Care Sciences and Society Karolinska Institutet Stockholm 14183 Sweden

Memory Clinic Department of Neurology 2nd Faculty of Medicine Charles University and Motol University Hospital Prague 15006 Czech Republic

Memory Clinic Skåne University Hospital Malmö 21428 Sweden

See more in PubMed

Hansson O. Biomarkers for neurodegenerative diseases. Nature Medicine. 2021 27:6 [Internet]. 2021 [cited 2023 Feb 27];27:954–63. https://www.nature.com/articles/s41591-021-01382-x. PubMed

Mattsson-Carlgren N, Andersson E, Janelidze S, Ossenkoppele R, Insel P, Strandberg O et al. Aβ deposition is associated with increases in soluble and phosphorylated tau that precede a positive Tau PET in Alzheimer’s disease. Sci Adv [Internet]. 2020 [cited 2023 Mar 24];6. https://pubmed.ncbi.nlm.nih.gov/32426454/. PubMed PMC

Aisen PS, Cummings J, Jack CR, Morris JC, Sperling R, Frölich L et al. On the path to 2025: Understanding the Alzheimer’s disease continuum. Alzheimers Res Ther [Internet]. 2017 [cited 2023 Mar 24];9:1–10. https://alzres.biomedcentral.com/articles/10.1186/s13195-017-0283-5. PubMed PMC

Sperling RA, Jack CR, Aisen PS. Testing the right target and right drug at the right stage [Internet]. Sci Transl Med. Sci Transl Med; 2011 [cited 2020 Jul 14]. https://pubmed.ncbi.nlm.nih.gov/22133718/. PubMed PMC

Cummings J, Feldman HH, Scheltens P. The rights of precision drug development for Alzheimer’s disease. Alzheimer’s Research & Therapy 2019 11:1 [Internet]. 2019 [cited 2023 Mar 22];11:1–14. https://alzres.biomedcentral.com/articles/10.1186/s13195-019-0529-5. PubMed PMC

Assunção SS, Sperling RA, Ritchie C, Kerwin DR, Aisen PS, Lansdall C et al. Meaningful benefits: a framework to assess disease-modifying therapies in preclinical and early Alzheimer’s disease. Alzheimers Res Ther [Internet]. 2022 [cited 2023 Mar 22];14. https://pubmed.ncbi.nlm.nih.gov/35440022/. PubMed PMC

Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chételat G et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement [Internet]. 2014 [cited 2017 Apr 19];10:844–52. http://www.ncbi.nlm.nih.gov/pubmed/24798886. PubMed PMC

Mitchell AJ, Beaumont H, Ferguson D, Yadegarfar M, Stubbs B. Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: Meta-analysis. Acta Psychiatr Scand [Internet]. 2014 [cited 2019 Mar 29];130:439–51. http://www.ncbi.nlm.nih.gov/pubmed/25219393. PubMed

Slot RER, Sikkes SAM, Berkhof J, Brodaty H, Buckley R, Cavedo E et al. Subjective cognitive decline and rates of incident Alzheimer’s disease and non-Alzheimer’s disease dementia. Alzheimers Dement [Internet]. 2019 [cited 2023 Mar 23];15:465–76. https://pubmed.ncbi.nlm.nih.gov/30555032/. PubMed PMC

Dubois B, Villain N, Frisoni GB, Rabinovici GD, Sabbagh M, Cappa S et al. Clinical diagnosis of Alzheimer’s disease: recommendations of the International Working Group. Lancet Neurol [Internet]. 2021 [cited 2023 Mar 22];20:484–96. https://pubmed.ncbi.nlm.nih.gov/33933186/. PubMed PMC

Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia [Internet]. 2018 [cited 2018 Apr 17];14:535–62. http://linkinghub.elsevier.com/retrieve/pii/S1552526018300724. PubMed PMC

Westman E, Simmons A, Zhang Y, Muehlboeck J-S, Tunnard C, Liu Y et al. Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls. Neuroimage [Internet]. 2011 [cited 2019 Mar 7];54:1178–87. http://www.ncbi.nlm.nih.gov/pubmed/20800095. PubMed

Jessen F, Feyen L, Freymann K, Tepest R, Maier W, Heun R et al. Volume reduction of the entorhinal cortex in subjective memory impairment. Neurobiol Aging [Internet]. 2006 [cited 2019 Apr 5];27:1751–6. http://www.ncbi.nlm.nih.gov/pubmed/16309795. PubMed

Saykin A, Wishart H, Rabin L, Santulli R, Flashman L, West J et al. Older adults with cognitive complaints show brain atrophy similar to that of amnestic MCI $watermark-text $watermark-text $watermark-text. Neurology [Internet]. 2006 [cited 2019 Apr 5];67:834–42. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3488276/pdf/nihms414646.pdf. PubMed PMC

Peter J, Scheef L, Abdulkadir A, Boecker H, Heneka M, Wagner M et al. Gray matter atrophy pattern in elderly with subjective memory impairment. Alzheimer’s & Dementia [Internet]. 2014 [cited 2019 Apr 5];10:99–108. http://www.ncbi.nlm.nih.gov/pubmed/23867795. PubMed

Flier WM, Mutsaers BMAW-RAWE, Bollen ER, Admiraal-Behloul ELEM et al. F,. Memory complaints in patients with normal cognition are associated with smaller hippocampal volumes. J Neurol [Internet]. 2004 [cited 2019 Apr 5];251:671–5. http://www.ncbi.nlm.nih.gov/pubmed/15311341. PubMed

Buckley RF, Maruff P, Ames D, Bourgeat P, Martins RN, Masters CL et al. Subjective memory decline predicts greater rates of clinical progression in preclinical Alzheimer’s disease. Alzheimer’s and Dementia [Internet]. 2016 [cited 2019 Apr 5];12:796–804. http://www.ncbi.nlm.nih.gov/pubmed/26852195. PubMed

Morrison C, Dadar M, Shafiee N, Villeneuve S, Louis Collins D. Regional brain atrophy and cognitive decline depend on definition of subjective cognitive decline. Neuroimage Clin. 2022;33:102923. doi: 10.1016/j.nicl.2021.102923. PubMed DOI PMC

Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom [Internet]. 2002 [cited 2018 Sep 18];16:119–28. http://doi.wiley.com/10.1002/cem.695.

Spulber G, Simmons A, Muehlboeck J-S, Mecocci P, Vellas B, Tsolaki M et al. An MRI-based index to measure the severity of Alzheimer’s disease-like structural pattern in subjects with mild cognitive impairment. J Intern Med [Internet]. 2013 [cited 2018 Sep 25];273:396–409. http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. PubMed PMC

Aguilar C, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, et al. Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort. Front Aging Neurosci. 2014;6:145. doi: 10.3389/fnagi.2014.00145. PubMed DOI PMC

Ferreira D, Falahati F, Linden C, Buckley RF, Ellis KA, Savage G et al. A Disease Severity Index to identify individuals with subjective memory decline who will progress to mild cognitive impairment or dementia. Sci Rep [Internet]. 2017 [cited 2018 Sep 11];7. Available from: www.nature.com/scientificreports. PubMed PMC

Falahati F, Westman E, Simmons A. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J Alzheimers Dis [Internet]. 2014 [cited 2024 May 4];41:685–708. https://pubmed.ncbi.nlm.nih.gov/24718104/. PubMed

Marcotte C, Potvin O, Collins DL, Rheault S, Duchesne S. Brain atrophy and patch-based grading in individuals from the CIMA-Q study: a progressive continuum from subjective cognitive decline to AD. Sci Rep [Internet]. 2019 [cited 2020 Nov 10];9:13532. http://www.nature.com/articles/s41598-019-49914-3. PubMed PMC

Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement [Internet]. 2011 [cited 2017 Apr 19];7:270–9. http://www.ncbi.nlm.nih.gov/pubmed/21514249. PubMed PMC

Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Dage JL et al. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nature Medicine 2021 27:6 [Internet]. 2021 [cited 2023 Feb 24];27:1034–42. https://www.nature.com/articles/s41591-021-01348-z. PubMed

Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nature Medicine. 2020 26:3 [Internet]. 2020 [cited 2023 Feb 24];26:379–86. https://www.nature.com/articles/s41591-020-0755-1. PubMed

Berglund G, Elmstähl S, Janzon L, Larsson SA. The Malmo Diet and Cancer Study. Design and feasibility. J Intern Med [Internet]. 1993 [cited 2019 Mar 7];233:45–51. http://www.ncbi.nlm.nih.gov/pubmed/8429286. PubMed

Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res [Internet]. 1975 [cited 2019 Mar 7];12:189–98. http://www.ncbi.nlm.nih.gov/pubmed/1202204. PubMed

Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol [Internet]. 2005 [cited 2022 Oct 11];62:1160–3. https://pubmed.ncbi.nlm.nih.gov/16009779/. PubMed

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 2013 [cited 2022 Oct 11]; Available from: /record/2013-14907-000.

Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: A follow-up study. Lancet Neurology [Internet]. 2006 [cited 2019 Mar 19];5:228–34. http://www.ncbi.nlm.nih.gov/pubmed/16488378. PubMed

Mckhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging and the Alzheimer’s Association workgroup. Alzheimer’s & Dementia [Internet]. 2011 [cited 2017 Apr 19];1–7. https://www.alz.org/documents_custom/Diagnostic_Recommendations_Alz_proof.pdf. PubMed PMC

Weyer G, Erzigkeit H, Kanowski S, Ihl R, Hadler D. Alzheimer’s Disease Assessment Scale: reliability and validity in a multicenter clinical trial. Int Psychogeriatr [Internet]. 1997 [cited 2022 Nov 17];9:123–38. https://pubmed.ncbi.nlm.nih.gov/9309486/. PubMed

Reisberg B, Ferris SH, De Leon MJ, Crook T. The global deterioration scale for assessment of primary degenerative dementia. American Journal of Psychiatry [Internet]. 1982 [cited 2020 Jul 22];139:1136–9. https://pubmed.ncbi.nlm.nih.gov/7114305/. PubMed

Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease [Internet]. Nat Rev Neurol. 2010 [cited 2019 Mar 28]. pp. 131–44. http://www.ncbi.nlm.nih.gov/pubmed/20157306. PubMed

Vandermeeren M, Mercken M, Vanmechelen E, Six J, Voorde A, Martin J-J et al. Detection of Proteins in Normal and Alzheimer’s Disease Cerebrospinal Fluid with a Sensitive Sandwich Enzyme-Linked Immunosorbent Assay. J Neurochem [Internet]. 2006 [cited 2019 Mar 28];61:1828–34. 10.1111/j.1471-4159.1993.tb09823.x. PubMed

Muehlboeck J-S, Simmons A, Westman E, THE HIVE DB IMAGE DATA MANAGEMENT. AND ANALYSIS FRAMEWORK. Alzheimer’s & Dementia [Internet]. 2014 [cited 2019 Mar 28];10:P389. http://journal.frontiersin.org/article/10.3389/fninf.2013.00049/abstract.

Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage [Internet]. 2006 [cited 2017 Jun 14];31:968–80. http://www.ncbi.nlm.nih.gov/pubmed/16530430. PubMed

Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron [Internet]. 2002 [cited 2017 Jun 25];33:341–55. http://www.ncbi.nlm.nih.gov/pubmed/11832223. PubMed

Fox NC, Crum WR, Scahill RI, Stevens JM, Janssen JC, Rossor MN. Imaging of onset and progression of Alzheimer’s disease with voxel-compression mapping of serial magnetic resonance images. Lancet [Internet]. 2001 [cited 2023 Mar 23];358:201–5. https://pubmed.ncbi.nlm.nih.gov/11476837/. PubMed

Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol [Internet]. 1991 [cited 2017 Apr 19];82:239–59. http://www.ncbi.nlm.nih.gov/pubmed/1759558. PubMed

Wold H. Nonlinear Estimation by Partial Least Squares Procedures. Research Papers in Statistics: Festschrift for J Neyman. 1966. pp. 414–44.

Wold S, Sjöström M, Eriksson L. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems [Internet]. Elsevier; 2001 [cited 2019 Mar 6]. pp. 109–30. https://www.sciencedirect.com/science/article/pii/S0169743901001551?via%3Dihub.

Westman E, Muehlboeck JS, Simmons A, Combining. MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage [Internet]. 2012 [cited 2023 Jan 25];62:229–38. https://pubmed.ncbi.nlm.nih.gov/22580170/. PubMed

Westman E, Wahlund LO, Foy C, Poppe M, Cooper A, Murphy D et al. Combining MRI and MRS to distinguish between Alzheimer’s disease and healthy controls. J Alzheimers Dis [Internet]. 2010 [cited 2024 May 5];22:171–81. https://pubmed.ncbi.nlm.nih.gov/20847449/. PubMed

Westman E, Simmons A, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M et al. AddNeuroMed and ADNI: similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage [Internet]. 2011 [cited 2023 Jan 28];58:818–28. https://pubmed.ncbi.nlm.nih.gov/21763442/. PubMed

Westman E, Aguilar C, Muehlboeck JS, Simmons A. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer’s disease and mild cognitive impairment. Brain Topogr [Internet]. 2013 [cited 2024 May 5];26:9–23. https://pubmed.ncbi.nlm.nih.gov/22890700/. PubMed PMC

Falahati F, Ferreira D, Soininen H, Mecocci P, Vellas B, Tsolaki M et al. The Effect of Age Correction on Multivariate Classification in Alzheimer’s Disease, with a Focus on the Characteristics of Incorrectly and Correctly Classified Subjects. Brain Topogr [Internet]. 2016 [cited 2019 Mar 19];29:296–307. http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. PubMed PMC

Aguilar C, Westman E, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M et al. Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Res [Internet]. 2013 [cited 2024 May 5];212:89–98. https://pubmed.ncbi.nlm.nih.gov/23541334/. PubMed

Eriksson L, Umetrics AB. Multi- and megavariate data analysis [Internet]., Umetrics AB. 2006 [cited 2019 Mar 7]. http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A152566&dswid=-2654

Stone M. Cross-Validatory Choice and Assessment of Statistical Predictions [Internet]. Journal of the Royal Statistical Society. Series B (Methodological). WileyRoyal Statistical Society; 1974 [cited 2018 Sep 25]. pp. 111–47. https://www.jstor.org/stable/pdf/2984809.pdf.

Jack C, Twomey CK, Zinsmeister AR, Sharbrough FW, Petersen RC, Cascino GD. Anterior temporal lobes and hippocampal formations: Normative volumetric measurements from MR images in young adults. Radiology [Internet]. 1989 [cited 2018 Sep 26];172:549–54. http://www.ncbi.nlm.nih.gov/pubmed/2748838. PubMed

Voevodskaya O, Simmons A, Nordenskjöld R, Kullberg J, Ahlström H, Lind L et al. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease. Front Aging Neurosci [Internet]. 2014 [cited 2023 Jan 26];6. https://pubmed.ncbi.nlm.nih.gov/25339897/. PubMed PMC

Murray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW. Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 2011;10:785–96. doi: 10.1016/S1474-4422(11)70156-9. PubMed DOI PMC

Ferreira D, Verhagen C, Hernández-Cabrera JA, Cavallin L, Guo C-J, Ekman U et al. Distinct subtypes of Alzheimer’s disease based on patterns of brain atrophy: longitudinal trajectories and clinical applications. Sci Rep [Internet]. 2017 [cited 2017 Oct 3];7:46263. http://www.ncbi.nlm.nih.gov/pubmed/28417965. PubMed PMC

Duara R, Loewenstein DA, Shen Q, Barker W, Greig MT, Varon D et al. Regional patterns of atrophy on MRI in Alzheimer’s disease: Neuropsychological features and progression rates in the ADNI cohort. Adv Alzheimer Dis [Internet]. 2013 [cited 2024 Apr 22];2:135–47. http://www.scirp.org/Html/40375.html.

Ferreira D, Nordberg A, Westman E. Biological subtypes of Alzheimer disease: A systematic review and meta-analysis. Neurology [Internet]. 2020 [cited 2024 May 4];94:436. /pmc/articles/PMC7238917/. PubMed PMC

Poulakis K, Pereira JB, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I et al. Heterogeneous patterns of brain atrophy in Alzheimer’s disease. Neurobiol Aging [Internet]. 2018 [cited 2018 Aug 29];65:98–108. https://linkinghub.elsevier.com/retrieve/pii/S0197458018300174. PubMed

Jack CR, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS, et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimer’s Dement. 2017;13:205–16. doi: 10.1016/j.jalz.2016.08.005. PubMed DOI PMC

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a Nonparametric Approach. Biometrics. 1988;44:837. doi: 10.2307/2531595. PubMed DOI

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC et al. pROC: An open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics [Internet]. 2011 [cited 2022 Nov 19];12:1–8. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-77. PubMed PMC

Jack CR, Shiung MM, Gunter JL, O’Brien PC, Weigand SD, Knopman DS et al. Comparison of Different MRI Brain Atrophy Rate Measures with Clinical Disease Progression in AD. Neurology [Internet]. 2004 [cited 2022 Nov 23];62:591. /pmc/articles/PMC2730165/. PubMed PMC

Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D et al. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer’s Disease. PLoS One [Internet]. 2011 [cited 2023 Mar 22];6:e25446. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025446. PubMed PMC

Khedher L, Ramírez J, Górriz JM, Brahim A, Segovia F. Early diagnosis of Alzheimer׳s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing. 2015;151:139–50. doi: 10.1016/j.neucom.2014.09.072. DOI

Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim HS et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage [Internet]. 2009 [cited 2023 Mar 23];47:1476–86. https://pubmed.ncbi.nlm.nih.gov/19463957/. PubMed PMC

Khan RU, Tanveer M, Pachori RB. A novel method for the classification of Alzheimer’s disease from normal controls using magnetic resonance imaging. Expert Syst [Internet]. 2021 [cited 2023 Mar 23];38:e12566. https://onlinelibrary.wiley.com/doi/full/10.1111/exsy.12566.

Lovestone S, Francis P, Kloszewska I, Mecocci P, Simmons A, Soininen H et al. AddNeuroMed–the European collaboration for the discovery of novel biomarkers for Alzheimer’s disease. Ann N Y Acad Sci [Internet]. 2009 [cited 2023 Jan 31];1180:36–46. https://pubmed.ncbi.nlm.nih.gov/19906259/. PubMed

Asl EH, Ghazal M, Mahmoud A, Aslantas A, Shalaby A, Casanova M et al. Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front Biosci (Landmark Ed) [Internet]. 2018 [cited 2022 Dec 6];23:584–96. https://pubmed.ncbi.nlm.nih.gov/28930562/. PubMed

Gómez-Sancho M, Tohka J, Gómez-Verdejo V. Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magn Reson Imaging [Internet]. 2018 [cited 2022 Dec 6];50:84–95. https://pubmed.ncbi.nlm.nih.gov/29530541/. PubMed

Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI. Comput Biol Med [Internet]. 2018 [cited 2022 Dec 6];102:30–9. https://pubmed.ncbi.nlm.nih.gov/30245275/. PubMed

Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage [Internet]. 2015 [cited 2022 Dec 6];104:398–412. https://pubmed.ncbi.nlm.nih.gov/25312773/. PubMed PMC

Yue L, Hu D, Zhang H, Wen J, Wu Y, Li W et al. Prediction of 7-year’s conversion from subjective cognitive decline to mild cognitive impairment. Hum Brain Mapp [Internet]. 2021 [cited 2022 Nov 27];42:192–203. https://pubmed.ncbi.nlm.nih.gov/33030795/. PubMed PMC

Li A, Yue L, Xiao S, Liu M. Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment. Brain Imaging Behav [Internet]. 2022 [cited 2022 Nov 29];16:645–58. https://pubmed.ncbi.nlm.nih.gov/34491529/. PubMed

van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M et al. Lecanemab in Early Alzheimer’s Disease. N Engl J Med [Internet]. 2023 [cited 2023 Jan 25];388. https://pubmed.ncbi.nlm.nih.gov/36449413/. PubMed

Teunissen CE, Verberk IMW, Thijssen EH, Vermunt L, Hansson O, Zetterberg H et al. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol [Internet]. 2022 [cited 2023 Mar 23];21:66–77. http://www.thelancet.com/article/S1474442221003616/fulltext. PubMed

Braak H, Braak E. Staging of Alzheimer’s Disease-Related Neurofibrillary Changes. Neurobiol Aging [Internet]. 1995 [cited 2017 Jun 22];16:271–84. http://diu-ma2.fr/wp-content/uploads/2015/12/121_biblio_Duyckaerts_5.pdf. PubMed

Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage. 2015;115:117–37. doi: 10.1016/j.neuroimage.2015.04.042. PubMed DOI PMC

Teipel SJ, Flatz WH, Heinsen H, Bokde ALW, Schoenberg SO, Stöckel S et al. Measurement of basal forebrain atrophy in Alzheimer’s disease using MRI. Brain [Internet]. 2005 [cited 2017 Apr 20];128:2626–44. https://academic.oup.com/brain/article-lookup/doi/10.1093/brain/awh589. PubMed