• Min Jeong Kwon, Subin Lee, Jieun Park, Sungman Jo, Ji Won Han, Dae Jong Oh, Jun-Young Lee, Joon Hyuk Park, Jae Hyoung Kim, and Ki Woong Kim.
• Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Korea.
• J. Korean Med. Sci. 2023 Oct 23; 38 (41): e316e316.

BackgroundTexture 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.MethodsWe 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.ResultsCompared 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).ConclusionTexture may be a useful neuroimaging marker for early detection of svPPA in older adults and its differentiation from AD.© 2023 The Korean Academy of Medical Sciences.

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