Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn).

brain atrophy classification corticobasal degeneration corticobasal syndrome disease progression machine learning magnetic resonance imaging progressive supranuclear palsy

Journal

Frontiers in neurology
ISSN: 1664-2295
Titre abrégé: Front Neurol
Pays: Switzerland
ID NLM: 101546899

Informations de publication

Date de publication:
2022
Historique:
received: 14 11 2021
accepted: 31 01 2022
entrez: 14 3 2022
pubmed: 15 3 2022
medline: 15 3 2022
Statut: epublish

Résumé

Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)-a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.

Identifiants

pubmed: 35280291
doi: 10.3389/fneur.2022.814768
pmc: PMC8914081
doi:

Types de publication

Journal Article

Langues

eng

Pagination

814768

Subventions

Organisme : Medical Research Council
ID : MR/T027770/1
Pays : United Kingdom

Informations de copyright

Copyright © 2022 Saito, Kamagata, Wijeratne, Andica, Uchida, Takabayashi, Fujita, Akashi, Wada, Shimoji, Hori, Masutani, Alexander and Aoki.

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

Neurol Clin. 2007 Aug;25(3):761-81, vii
pubmed: 17659189
Mov Disord. 2001 Jul;16(4):656-67
pubmed: 11481689
Neuroimage. 2004 Oct;23(2):663-9
pubmed: 15488416
Acta Neuropathol. 2006 Sep;112(3):341-8
pubmed: 16804710
Neurology. 1999 Aug 11;53(3):502-7
pubmed: 10449111
AJNR Am J Neuroradiol. 2009 Nov;30(10):1884-92
pubmed: 19833793
J Neuropathol Exp Neurol. 2001 Jun;60(6):647-57
pubmed: 11398841
Ann Clin Transl Neurol. 2018 Apr 02;5(5):570-582
pubmed: 29761120
Brain. 2018 May 1;141(5):1529-1544
pubmed: 29579160
Neurology. 1993 Nov;43(11):2412-4
pubmed: 8232972
J Neuropathol Exp Neurol. 1996 Jan;55(1):97-105
pubmed: 8558176
Neuroimage. 2012 Aug 15;62(2):774-81
pubmed: 22248573
Neurology. 1996 Jul;47(1):1-9
pubmed: 8710059
Brain. 2007 Jun;130(Pt 6):1552-65
pubmed: 17405767
J Psychiatr Res. 1975 Nov;12(3):189-98
pubmed: 1202204
Mov Disord. 2003 Sep;18 Suppl 6:S13-20
pubmed: 14502651
J Gerontol. 1982 May;37(3):323-9
pubmed: 7069156
Brain. 2006 Apr;129(Pt 4):1040-9
pubmed: 16455792
Mov Disord. 2011 Jan;26(1):169-73
pubmed: 20836136
Ann Neurol. 2011 Aug;70(2):327-40
pubmed: 21823158
Mov Disord. 2003 May;18(5):467-86
pubmed: 12722160
Magn Reson Med Sci. 2004 Dec 15;3(3):125-32
pubmed: 16093629
Eur J Neurol. 2013 Oct;20(10):1417-22
pubmed: 23746093
Handb Clin Neurol. 2008;89:487-91
pubmed: 18631771
Brain Commun. 2020;2(1):fcaa051
pubmed: 32671340
Mov Disord. 2013 Jul;28(8):1117-24
pubmed: 23568852
Neuroradiology. 2017 May;59(5):431-443
pubmed: 28386688
Neuroimage. 2012 Apr 15;60(3):1880-9
pubmed: 22281676
J Neurosci Methods. 2014 Jun 15;230:37-50
pubmed: 24785589
J Neurol Sci. 2001 Jan 15;183(1):95-8
pubmed: 11166802
Brain. 2016 Dec;139(Pt 12):3237-3252
pubmed: 27797812
Arch Neurol. 2006 Jan;63(1):81-6
pubmed: 16401739
Mov Disord. 2012 Dec;27(14):1754-62
pubmed: 22488922
Mov Disord. 2017 Jul;32(7):955-971
pubmed: 28500751
Neurology. 2003 Jun 10;60(11):1766-9
pubmed: 12796528
Neurology. 2013 Jan 29;80(5):496-503
pubmed: 23359374
PLoS One. 2016 Jun 16;11(6):e0157218
pubmed: 27310132
J Neurol. 2019 Jul;266(7):1771-1781
pubmed: 31037416
Brain Behav. 2015 Jun;5(6):e00329
pubmed: 26085961
Neurology. 2010 Nov 23;75(21):1879-87
pubmed: 21098403
Neurology. 2006 Mar 28;66(6):949-50
pubmed: 16567726
Mov Disord Clin Pract. 2020 Apr 10;7(4):440-447
pubmed: 32373661
J Neurol Neurosurg Psychiatry. 2014 Aug;85(8):925-9
pubmed: 24521567
Brain. 2011 Nov;134(Pt 11):3264-75
pubmed: 21933807
Mov Disord. 2008 Nov 15;23(15):2129-70
pubmed: 19025984
Lancet Neurol. 2009 Mar;8(3):270-9
pubmed: 19233037
Mov Disord Clin Pract. 2018 Mar 06;5(2):145-148
pubmed: 30363457
Neuroradiology. 2019 Nov;61(11):1333-1339
pubmed: 31520153
Intern Med. 2011;50(22):2775-81
pubmed: 22082889
Am J Pathol. 2002 Jun;160(6):2045-53
pubmed: 12057909
Neurology. 2016 Nov 8;87(19):2016-2025
pubmed: 27742814
Neurobiol Aging. 2008 Feb;29(2):280-9
pubmed: 17097770
J Am Geriatr Soc. 2005 Apr;53(4):695-9
pubmed: 15817019
Curr Neurol Neurosci Rep. 2018 Feb 17;18(3):12
pubmed: 29455271
Brain. 2014 Sep;137(Pt 9):2564-77
pubmed: 25012224
Nat Commun. 2018 Oct 15;9(1):4273
pubmed: 30323170
Neuroimage. 2004 Feb;21(2):714-24
pubmed: 14980574
Lancet Neurol. 2014 Jul;13(7):676-85
pubmed: 24873720
J Neuropathol Exp Neurol. 2002 Nov;61(11):935-46
pubmed: 12430710
Mov Disord. 2010 Jul 15;25(9):1246-52
pubmed: 20629131
Brain. 2007 Jun;130(Pt 6):1566-76
pubmed: 17525140
Parkinsonism Relat Disord. 2018 Sep;54:90-94
pubmed: 29643007
Acta Neuropathol. 2020 Aug;140(2):99-119
pubmed: 32383020

Auteurs

Yuya Saito (Y)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Koji Kamagata (K)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Peter A Wijeratne (PA)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Christina Andica (C)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Wataru Uchida (W)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Kaito Takabayashi (K)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Shohei Fujita (S)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

Toshiaki Akashi (T)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Akihiko Wada (A)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Keigo Shimoji (K)

Department of Radiology, Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan.

Masaaki Hori (M)

Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan.

Yoshitaka Masutani (Y)

Department of Biomedical Information Sciences, Hiroshima City University Graduate School of Information Sciences, Hiroshima, Japan.

Daniel C Alexander (DC)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Shigeki Aoki (S)

Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Classifications MeSH