Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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Assessment of Mild Cognitive Impairment in Elderly Subjects Using a Fully Automated Brain Segmentation Software

  • Kwon, Chiheon (Department of Radiology, Seoul National University Hospital) ;
  • Kang, Koung Mi (Department of Radiology, Seoul National University Hospital) ;
  • Byun, Min Soo (Department of Psychiatry, Pusan National University Yangsan Hospital) ;
  • Yi, Dahyun (Institute of Human Behavioral Medicine, Seoul National University Medical Research Center) ;
  • Song, Huijin (Biomedical Research Institute, Seoul National University Hospital) ;
  • Lee, Ji Ye (Department of Radiology, Seoul National University Hospital) ;
  • Hwang, Inpyeong (Department of Radiology, Seoul National University Hospital) ;
  • Yoo, Roh-Eul (Department of Radiology, Seoul National University Hospital) ;
  • Yun, Tae Jin (Department of Radiology, Seoul National University Hospital) ;
  • Choi, Seung Hong (Department of Radiology, Seoul National University Hospital) ;
  • Kim, Ji-hoon (Department of Radiology, Seoul National University Hospital) ;
  • Sohn, Chul-Ho (Department of Radiology, Seoul National University Hospital) ;
  • Lee, Dong Young (Department of Neuropsychiatry, Seoul National University Hospital)
  • Received : 2021.04.26
  • Accepted : 2021.06.21
  • Published : 2021.09.30
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Abstract

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

Keywords

Acknowledgement

This study was supported by a Scientific Research Fund of the Korean Society of Magnetic Resonance in Medicine (Grant no. 06-20191860) and a SNUH Research Fund (Grant no. 04-20190500). This work was also supported by a Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: KMDF_PR_20200901_0062, 9991006735).

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