Assessment of Mild Cognitive Impairment in Elderly Subjects Using a Fully Automated Brain Segmentation Software
10.13104/imri.2021.25.3.164
- Author:
Chiheon KWON
1
;
Koung Mi KANG
;
Min Soo BYUN
;
Dahyun YI
;
Huijin SONG
;
Ji Ye LEE
;
Inpyeong HWANG
;
Roh-Eul YOO
;
Tae Jin YUN
;
Seung Hong CHOI
;
Ji-hoon KIM
;
Chul-Ho SOHN
;
Dong Young LEE
;
Author Information
1. Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Publication Type:Original Article
- From:Investigative Magnetic Resonance Imaging
2021;25(3):164-171
- CountryRepublic of Korea
- Language:English
-
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.
