Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion.
ADNI
clinical features
deep learning
longitudinal
mild cognitive impairment
multimodal
neuroimaging
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2021
2021
Historique:
received:
19
07
2021
accepted:
09
11
2021
entrez:
20
1
2022
pubmed:
21
1
2022
medline:
21
1
2022
Statut:
epublish
Résumé
Alzheimer's disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient's ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (
Identifiants
pubmed: 35046766
doi: 10.3389/fnins.2021.744190
pmc: PMC8761739
doi:
Types de publication
Journal Article
Langues
eng
Pagination
744190Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR003167
Pays : United States
Informations de copyright
Copyright © 2022 Pena, Suescun, Schiess, Ellmore, Giancardo and the Alzheimer’s Disease Neuroimaging Initiative.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Dement Geriatr Cogn Disord. 2017;43(3-4):144-154
pubmed: 28152536
Neuroimage Clin. 2019;22:101771
pubmed: 30927601
Neuroimage Clin. 2016 Dec 18;13:415-427
pubmed: 28116234
Neurology. 2010 Jul 20;75(3):230-8
pubmed: 20592257
Neuroimage Clin. 2018 Aug 10;20:506-522
pubmed: 30167371
Neuroimage Clin. 2020;26:102199
pubmed: 32106025
Neuroimage. 2019 Apr 1;189:276-287
pubmed: 30654174
Stroke. 2019 Nov;50(11):3093-3100
pubmed: 31547796
J Alzheimers Dis. 2017;59(4):1359-1379
pubmed: 28759968
Int J Geriatr Psychiatry. 2003 Nov;18(11):1021-8
pubmed: 14618554
Front Aging Neurosci. 2017 Apr 05;9:86
pubmed: 28424612
IEEE Rev Biomed Eng. 2019;12:19-33
pubmed: 30561351
IEEE Trans Biomed Eng. 2017 Jan;64(1):155-165
pubmed: 27046891
Neuroimage. 2015 Jan 1;104:398-412
pubmed: 25312773
Behav Neurol. 2017;2017:6364314
pubmed: 29085184
Eur J Neurol. 2018 Jan;25(1):59-70
pubmed: 28872215
Brain Behav. 2018 Mar 06;8(4):e00942
pubmed: 29670824
Comput Intell Neurosci. 2015;2015:865265
pubmed: 26101520
Front Genet. 2019 Jun 28;10:617
pubmed: 31316553
Neuroimage. 2017 Jul 15;155:530-548
pubmed: 28414186
Front Neurosci. 2019 Oct 04;13:1053
pubmed: 31636533
Brain. 2018 Jul 1;141(7):1917-1933
pubmed: 29850777
Comput Med Imaging Graph. 2019 Apr;73:1-10
pubmed: 30763637
J Psychiatr Res. 2018 Jan;96:33-38
pubmed: 28957712
J Cogn Neurosci. 1996 Nov;8(6):566-87
pubmed: 23961985
Med Image Anal. 2021 Jan;67:101848
pubmed: 33091740
Neurobiol Aging. 2010 Aug;31(8):1364-74
pubmed: 20570399
Cereb Cortex. 2011 Aug;21(8):1870-8
pubmed: 21239393
Front Hum Neurosci. 2017 Feb 06;11:33
pubmed: 28220065
Front Neuroinform. 2017 Feb 24;11:16
pubmed: 28286479
Brain. 2014 Sep;137(Pt 9):2564-77
pubmed: 25012224
Cereb Cortex. 2020 Jan 10;30(1):326-338
pubmed: 31169867
Lancet Neurol. 2010 Jan;9(1):119-28
pubmed: 20083042
Sci Rep. 2019 Feb 13;9(1):1952
pubmed: 30760848
PLoS One. 2015 Jul 10;10(7):e0130140
pubmed: 26161953
Ann Intern Med. 2008 Mar 18;148(6):427-34
pubmed: 18347351
Neurobiol Dis. 2017 Sep;105:33-41
pubmed: 28511918
Neuroimage. 2012 Oct 15;63(1):320-7
pubmed: 22776459
J Neurosci Methods. 2019 Jul 15;323:108-118
pubmed: 31132373
Dement Geriatr Cogn Disord. 2017;43(1-2):1-14
pubmed: 27889770
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
Radiology. 2018 Jul;288(1):198-206
pubmed: 29762090
J Neurol Neurosurg Psychiatry. 2016 Apr;87(4):425-32
pubmed: 25904810
Int J Biomed Imaging. 2018 Mar 15;2018:1247430
pubmed: 29736165
Front Neurol. 2019 Jul 16;10:756
pubmed: 31379711
PLoS Comput Biol. 2018 Sep 14;14(9):e1006376
pubmed: 30216352