{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:19:46Z","timestamp":1787026786089,"version":"build-2736575974"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate machine learning models (MLMs) to diagnose Alzheimer\u2019s disease (AD) using cortical complexity indicated by fractal dimension (FD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 296 participants with normal cognitive (NC) function and 182 with AD from the AD Neuroimaging Initiative database were randomly divided into training and internal validation cohorts. Then, FDs, demographic characteristics, baseline global cognitive function scales [Montreal Cognitive Assessment (MoCA), Functional Activities Questionnaire (FAQ), Global Deterioration Scale (GDS), Neuropsychiatric Inventory (NPI)], phospho-tau (p-tau 181), amyloid\u03b2-42\/40, apolipoprotein E (APOE) and polygenic hazard score (PHS) were collected to establish multiple MLMs. Receiver operating characteristic curves were used to evaluate model performance. Participants from our institution (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200966; 33 with NC and 33 with AD) served as external validation cohorts to validate the MLMs. Decision curve analysis was used to estimate the models\u2019 clinical values.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The FDs from 30 out of 69 regions showed significant alteration. All MLMs were conducted based on the 30 significantly different FDs. The FD model had good accuracy in predicting AD in three cohorts [area under the receiver operating characteristic (ROC) curve (AUC)\u2009=\u20090.842, 0.808, and 0.803]. There were no statistically significant differences in AUC values between the FD model and the other combined models in the training and internal validation cohorts except MoCA + FD and FAQ\u2009+\u2009FD models. Among MLMs, the MoCA + FD model showed the best predictive efficiency in three cohorts (AUC\u2009=\u20090.951, 0.931, and 0.955) and had the highest clinical net benefit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The FD model showed favorable diagnostic performance for AD. Among MLMs, the MoCA + FD model can predict AD with the highest efficiency and could be used as a non-invasive diagnostic method.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2024.1434589","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:56:03Z","timestamp":1728435363000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine learning models for diagnosing Alzheimer\u2019s disease using brain cortical complexity"],"prefix":"10.3389","volume":"16","author":[{"given":"Shaofan","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiji","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rifeng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjing","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.nicl.2013.12.012","article-title":"ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease","volume":"4","author":"Apostolova","year":"2014","journal-title":"NeuroImage: Clinical"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/biomedicines11010149","article-title":"Hybridized deep learning approach for detecting Alzheimer's disease","volume":"11","author":"Balaji","year":"2023","journal-title":"Biomedicines"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1006\/nimg.2001.0848","article-title":"In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease","volume":"14","author":"Baron","year":"2001","journal-title":"NeuroImage"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1201617","DOI":"10.3389\/fphys.2023.1201617","article-title":"Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning","volume":"14","author":"Battalapalli","year":"2023","journal-title":"Front. Physiol."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"4987","DOI":"10.1002\/alz.13083","article-title":"Comparing machine learning-derived MRI-based and blood-based neurodegeneration biomarkers in predicting syndromal conversion in early AD","volume":"19","author":"Cai","year":"2023","journal-title":"Alzheimers Dement."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3390\/jpm12010089","article-title":"Aberrant structure MRI in Parkinson\u2019s disease and comorbidity with depression based on multinomial tensor regression analysis","volume":"12","author":"Cao","year":"2022","journal-title":"J. Pers. Med."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.3390\/ijms22052761","article-title":"Machine learning and novel biomarkers for the diagnosis of Alzheimer\u2019s disease","volume":"22","author":"Chang","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e26529","DOI":"10.1002\/hbm.26529","article-title":"Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: inter-cohort validation of Shanghai memory study and ADNI","volume":"45","author":"Chen","year":"2023","journal-title":"Hum. Brain Mapp."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1021\/acschemneuro.2c00255","article-title":"Machine learning-based classification of subjective cognitive decline, mild cognitive impairment, and Alzheimer\u2019s dementia using Neuroimage and plasma biomarkers","volume":"13","author":"Chiu","year":"2022","journal-title":"ACS Chem. Neurosci."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","article-title":"RNN-based longitudinal analysis for diagnosis of Alzheimer's disease","volume":"73","author":"Cui","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"e958","DOI":"10.1212\/wnl.0000000000013228","article-title":"Association of CSF A\u03b2 38 levels with risk of Alzheimer disease\u2013related decline","volume":"98","author":"Cullen","year":"2022","journal-title":"Neurology"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1080\/13825585.2021.1958139","article-title":"Cortical complexity alterations in the medial temporal lobe are associated with Alzheimer's disease psychosis","volume":"29","author":"D'Antonio","year":"2022","journal-title":"Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"NeuroImage"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1007\/s00415-006-0435-1","article-title":"Episodic memory impairment in patients with Alzheimer's disease is correlated with entorhinal cortex atrophy. A voxel-based morphometry study","volume":"254","author":"Di Paola","year":"2007","journal-title":"J. Neurol."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"e0237587","DOI":"10.1371\/journal.pone.0237587","article-title":"FeAture explorer (FAE): a tool for developing and comparing radiomics models","volume":"15","author":"Dimitriadis","year":"2020","journal-title":"PLoS One"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1002\/alz.12700","article-title":"A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China aging and neurodegenerative initiative (CANDI) study","volume":"19","author":"Gao","year":"2022","journal-title":"Alzheimers Dement."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1177\/0891988714524630","article-title":"Validity of the Montreal cognitive assessment as a screen for mild cognitive impairment and dementia in African Americans","volume":"27","author":"Goldstein","year":"2014","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"901419","DOI":"10.3389\/fdgth.2022.901419","article-title":"Spontaneous speech feature analysis for alzheimer's disease screening using a random forest classifier","volume":"4","author":"Hason","year":"2022","journal-title":"Front. Digit. Health"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"102409","DOI":"10.1016\/j.eclinm.2023.102409","article-title":"Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study","volume":"68","author":"Hu","year":"2024","journal-title":"eClinicalMedicine"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3390\/brainsci11010107","article-title":"Using fractal dimension analysis with the Desikan\u2013Killiany atlas to assess the effects of Normal aging on subregional cortex alterations in adulthood","volume":"11","author":"Jao","year":"2021","journal-title":"Brain Sci."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1038\/s41582-022-00665-2","article-title":"Blood phospho-tau in Alzheimer disease: analysis, interpretation, and clinical utility","volume":"18","author":"Karikari","year":"2022","journal-title":"Nat. Rev. Neurol."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"818871","DOI":"10.3389\/fnagi.2022.818871","article-title":"Alzheimer\u2019s disease diagnosis and biomarker analysis using resting-state functional MRI functional brain network with multi-measures features and hippocampal subfield and amygdala volume of structural MRI","volume":"14","author":"Khatri","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.neuroimage.2010.06.050","article-title":"Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease","volume":"53","author":"King","year":"2010","journal-title":"NeuroImage"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"e51473","DOI":"10.2196\/51473","article-title":"Machine learning\u2013based prediction of suicidality in adolescents with allergic rhinitis: derivation and validation in 2 independent Nationwide cohorts","volume":"26","author":"Lee","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"e14408","DOI":"10.15252\/emmm.202114408","article-title":"Blood-based biomarkers for Alzheimer's disease","volume":"14","author":"Leuzy","year":"2021","journal-title":"EMBO Mol. Med."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1002\/hbm.22741","article-title":"View-centralized multi-atlas classification for Alzheimer's disease diagnosis","volume":"36","author":"Liu","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"741","DOI":"10.21037\/atm-22-630","article-title":"Surface-based morphological patterns associated with neuropsychological performance, symptom severity, and treatment response in Parkinson\u2019s disease","volume":"10","author":"Mo","year":"2022","journal-title":"Annal. Transl. Med."},{"key":"ref29","first-page":"47","article-title":"Changes in the volume of temporal lobe structures related to Alzheimer's type dementia","volume":"40","author":"Morys","year":"2002","journal-title":"Folia Neuropathol."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1001\/jamaneurol.2020.4986","article-title":"Longitudinal associations of blood phosphorylated Tau181 and Neurofilament light chain with neurodegeneration in Alzheimer disease","volume":"78","author":"Moscoso","year":"2021","journal-title":"JAMA Neurol."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3233\/JAD-200246","article-title":"Cortical complexity analyses and their cognitive correlate in Alzheimer's disease and frontotemporal dementia","volume":"76","author":"Nicastro","year":"2020","journal-title":"J. Alzheimers Dis."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"e105","DOI":"10.1016\/s2468-2667(21)00249-8","article-title":"Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019","volume":"7","author":"Nichols","year":"2022","journal-title":"Lancet Public Health"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"101990","DOI":"10.1016\/j.nicl.2019.101990","article-title":"Fractal dimension of cerebral white matter: a consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment","volume":"24","author":"Pantoni","year":"2019","journal-title":"Neuroimage Clin"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1093\/bioinformatics\/btad534","article-title":"Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies","volume":"39","author":"Park","year":"2023","journal-title":"Bioinformatics"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"935055","DOI":"10.3389\/fnagi.2022.935055","article-title":"Hierarchical multi-class Alzheimer\u2019s disease diagnostic framework using imaging and clinical features","volume":"14","author":"Qin","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.3390\/diagnostics13091521","article-title":"Quantitative brain MRI metrics distinguish four different ALS phenotypes: a machine learning based study","volume":"13","author":"Rajagopalan","year":"2023","journal-title":"Diagnostics"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1186\/s13024-022-00574-4","article-title":"ApoE in Alzheimer's disease: pathophysiology and therapeutic strategies","volume":"17","author":"Raulin","year":"2022","journal-title":"Mol. Neurodegener."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.neuroimage.2014.06.054","article-title":"Structural brain complexity and cognitive decline in late life \u2014 a longitudinal study in the Aberdeen 1936 birth cohort","volume":"100","author":"Sandu","year":"2014","journal-title":"NeuroImage"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"e12321","DOI":"10.1002\/trc2.12321","article-title":"Alzheimer's polygenic hazard score in SuperAgers: SuperGenes or SuperResilience?","volume":"8","author":"Spencer","year":"2022","journal-title":"Alzheimer's Dementia Transl. Res. Clin. Interv."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"e0155415","DOI":"10.1371\/journal.pone.0155415","article-title":"Fractal dimension analysis of subcortical gray matter structures in schizophrenia","volume":"11","author":"Stamatakis","year":"2016","journal-title":"PLoS One"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1186\/s12864-022-08617-2","article-title":"Assessment of a polygenic hazard score for the onset of pre-clinical Alzheimer\u2019s disease","volume":"23","author":"Vacher","year":"2022","journal-title":"BMC Genomics"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"101859","DOI":"10.1016\/j.nicl.2019.101859","article-title":"Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes","volume":"23","author":"Wang","year":"2019","journal-title":"NeuroImage: Clin."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.2147\/ndt.S212328","article-title":"Comparative diagnostic accuracy of ACE-III and MoCA for detecting mild cognitive impairment","volume":"15","author":"Wang","year":"2019","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2009.07.042","article-title":"Fractal dimension analysis for quantifying cerebellar morphological change of multiple system atrophy of the cerebellar type (MSA-C)","volume":"49","author":"Wu","year":"2010","journal-title":"NeuroImage"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"808520","DOI":"10.3389\/fnagi.2022.808520","article-title":"Machine learning models for diagnosis of Parkinson's disease using multiple structural magnetic resonance imaging features","volume":"14","author":"Ya","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1186\/s12911-023-02238-9","article-title":"XGBoost-SHAP-based interpretable diagnostic framework for alzheimer\u2019s disease","volume":"23","author":"Yi","year":"2023","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-642-15745-5_21","article-title":"Estimating local surface complexity maps using spherical harmonic reconstructions","volume":"13","author":"Yotter","year":"2010","journal-title":"Med. Image Comput. Assist. Interv."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1040001","DOI":"10.3389\/fnagi.2022.1040001","article-title":"Predicting AT(N) pathologies in Alzheimer\u2019s disease from blood-based proteomic data using neural networks","volume":"14","author":"Zhang","year":"2022","journal-title":"Front. Aging Neurosci."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2024.1434589\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:56:06Z","timestamp":1728435366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2024.1434589\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"references-count":48,"alternative-id":["10.3389\/fnagi.2024.1434589"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2024.1434589","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,9]]},"article-number":"1434589"}}