Automated Brain MRI Volumetry Differentiates Early Stages of Alzheimer's Disease From Normal Aging. (November 2019)

• Record Type: Journal Article
• Authors: Zhao, Weina; Luo, Yishan; Zhao, Lei; Mok, Vincent; Su, Li; Yin, Changhao; Sun, Yu; Lu, Jie; Shi, Lin; Han, Ying
• Other Names: Xiang Yu-Tao guest-editor.
• Is Part Of: Journal of geriatric psychiatry and neurology. Volume 32:Number 6(2019)
• Page Start: 354
• Page End: 364
• Publication Date: 2019-11
• Subjects: subjective cognitive decline -- amnestic mild cognitive impairment -- Alzheimer disease -- magnetic resonance imaging -- brain volumetry; Geriatric neurology -- Periodicals; Geriatric neuropsychiatry -- Periodicals; Geriatric psychiatry -- Periodicals; Nervous system -- Diseases -- Periodicals; 618.97689
• DOI: 10.1177/0891988719862637
• Languages: English
• ISSNs: 0891-9887

Abstract

As an enrichment strategy supplemented by the diagnostic framework of subjective cognitive decline (SCD), SCD plus identifies features that may increase the likelihood of including future-Alzheimer's disease (AD) patients. This study aimed to identify the shared and distinct atrophy patterns between patients specified by SCD plus and amnestic mild cognitive impairment (aMCI, a prodromal stage of AD) and to investigate the extent that automated brain magnetic resonance imaging (MRI) volumetry can differentiate patients with SCD from normal control (NC) participants and patients with aMCI. We acquired structural MRI brain scans from 44 patients with aMCI, 40 patients with SCD (who met the major criteria of SCD plus ), and 48 NC participants. Automatic brain segmentation was performed to quantify the volumetric measures of cognitive-relevant areas. These volumetric measures were compared across the 3 groups with analysis of variance. In addition, we performed support vector machine analyses using volumetric measures of single regions or multiple regions to further evaluate the sensitivity of automated brain volumetry in differentiating a specific group from another. The atrophy patterns in patients with aMCI and SCD were similar. Using the regional volumetric measures, we achieved high performance in differentiating aMCI and SCD from NCs (average classification accuracy [ACC] > 90%). However, the performance was not ideal when differentiating aMCI from SCD (ACC < 63%). In conclusion, patients with SCD specified by SCD plus presented similar atrophy patterns as patients with aMCI, which was distinguishable from NC participants. Future studies should aim to associate the atrophy patterns of SCD with possible conversion to aMCI or AD in a longitudinal design.