Textural and Volumetric Changes of the Temporal Lobes in Semantic Variant Primary Progressive Aphasia and Alzheimer's Disease.


Journal

Journal of Korean medical science
ISSN: 1598-6357
Titre abrégé: J Korean Med Sci
Pays: Korea (South)
ID NLM: 8703518

Informations de publication

Date de publication:
23 Oct 2023
Historique:
received: 07 04 2023
accepted: 15 06 2023
medline: 30 10 2023
pubmed: 24 10 2023
entrez: 24 10 2023
Statut: epublish

Résumé

Texture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic variant primary progressive aphasia (svPPA) and Alzheimer's disease (AD) by comparing the temporal gray matter texture and volume between cognitively normal controls and older adults with svPPA and AD. We enrolled all participants from three university hospitals in Korea. We obtained T1-weighted magnetic resonance images and compared the gray matter texture and volume of regions of interest (ROIs) between the groups using analysis of variance with Bonferroni posthoc comparisons. We also developed models for classifying svPPA, AD and control groups using logistic regression analyses, and validated the models using receiver operator characteristics analysis. Compared to the AD group, the svPPA group showed lower volumes in five ROIs (bilateral temporal poles, and the left inferior, middle, and superior temporal cortices) and higher texture in these five ROIs and two additional ROIs (right inferior temporal and left entorhinal cortices). The performances of both texture- and volume-based models were good and comparable in classifying svPPA from normal cognition (mean area under the curve [AUC] = 0.914 for texture; mean AUC = 0.894 for volume). However, only the texture-based model achieved a good level of performance in classifying svPPA and AD (mean AUC = 0.775 for texture; mean AUC = 0.658 for volume). Texture may be a useful neuroimaging marker for early detection of svPPA in older adults and its differentiation from AD.

Sections du résumé

BACKGROUND BACKGROUND
Texture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic variant primary progressive aphasia (svPPA) and Alzheimer's disease (AD) by comparing the temporal gray matter texture and volume between cognitively normal controls and older adults with svPPA and AD.
METHODS METHODS
We enrolled all participants from three university hospitals in Korea. We obtained T1-weighted magnetic resonance images and compared the gray matter texture and volume of regions of interest (ROIs) between the groups using analysis of variance with Bonferroni posthoc comparisons. We also developed models for classifying svPPA, AD and control groups using logistic regression analyses, and validated the models using receiver operator characteristics analysis.
RESULTS RESULTS
Compared to the AD group, the svPPA group showed lower volumes in five ROIs (bilateral temporal poles, and the left inferior, middle, and superior temporal cortices) and higher texture in these five ROIs and two additional ROIs (right inferior temporal and left entorhinal cortices). The performances of both texture- and volume-based models were good and comparable in classifying svPPA from normal cognition (mean area under the curve [AUC] = 0.914 for texture; mean AUC = 0.894 for volume). However, only the texture-based model achieved a good level of performance in classifying svPPA and AD (mean AUC = 0.775 for texture; mean AUC = 0.658 for volume).
CONCLUSION CONCLUSIONS
Texture may be a useful neuroimaging marker for early detection of svPPA in older adults and its differentiation from AD.

Identifiants

pubmed: 37873627
pii: 38.e316
doi: 10.3346/jkms.2023.38.e316
pmc: PMC10593601
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e316

Subventions

Organisme : Ministry of Health and Welfare
ID : HI09C1379 [A092077]
Pays : Korea
Organisme : Ministry of Science and ICT, South Korea
ID : HU20C0015
Pays : Korea

Informations de copyright

© 2023 The Korean Academy of Medical Sciences.

Déclaration de conflit d'intérêts

The authors have no potential conflicts of interest to disclose.

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Auteurs

Min Jeong Kwon (MJ)

Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Korea.

Subin Lee (S)

Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Korea.

Jieun Park (J)

Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Korea.

Sungman Jo (S)

Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.

Ji Won Han (JW)

Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.
Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea.

Dae Jong Oh (DJ)

Workplace Mental Health Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.

Jun-Young Lee (JY)

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea.
Department of Neuropsychiatry, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea.

Joon Hyuk Park (JH)

Department of Neuropsychiatry, Jeju National University Hospital, Jeju, Korea.

Jae Hyoung Kim (JH)

Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.

Ki Woong Kim (KW)

Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Korea.
Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.
Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea. kwkimmd@snu.ac.kr.

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Classifications MeSH