Literature DB >> 35697957

Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer's disease dementia.

Dong-Woo Ryu1, Yun Jeong Hong1, Jung Hee Cho1, Kichang Kwak2, Jong-Min Lee2, Yong S Shim1, Young Chul Youn3, Dong Won Yang4.   

Abstract

A quantitative analysis of brain volume can assist in the diagnosis of Alzheimer's disease (AD) which is ususally accompanied by brain atrophy. With an automated analysis program Quick Brain Volumetry (QBraVo) developed for volumetric measurements, we measured regional volumes and ratios to evaluate their performance in discriminating AD dementia (ADD) and mild cognitive impairment (MCI) patients from normal controls (NC). Validation of QBraVo was based on intra-rater and inter-rater reliability with a manual measurement. The regional volumes and ratios to total intracranial volume (TIV) and to total brain volume (TBV) or total cerebrospinal fluid volume (TCV) were compared among subjects. The regional volume to total cerebellar volume ratio named Standardized Atrophy Volume Ratio (SAVR) was calculated to compare brain atrophy. Diagnostic performances to distinguish among NC, MCI, and ADD were compared between MMSE, SAVR, and the predictive model. In total, 56 NCs, 44 MCI, and 45 ADD patients were enrolled. The average run time of QBraVo was 5 min 36 seconds. Intra-rater reliability was 0.999. Inter-rater reliability was high for TBV, TCV, and TIV (R = 0.97, 0.89 and 0.93, respectively). The medial temporal SAVR showed the highest performance for discriminating ADD from NC (AUC = 0.808, diagnostic accuracy = 80.2%). The predictive model using both MMSE and medial temporal SAVR improved the diagnostic performance for MCI in NC (AUC = 0.844, diagnostic accuracy = 79%). Our results demonstrated QBraVo is a fast and accurate method to measure brain volume. The regional volume calculated as SAVR could help to diagnose ADD and MCI and increase diagnostic accuracy for MCI.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Alzheimer’s disease; Cognitive dysfunction; Computer-assisted image processing; Dementia; Magnetic resonance imaging

Mesh:

Year:  2022        PMID: 35697957     DOI: 10.1007/s11682-022-00678-x

Source DB:  PubMed          Journal:  Brain Imaging Behav        ISSN: 1931-7557            Impact factor:   3.224


  5 in total

1.  Alterations in white matter volume and its correlation with neuropsychological scales in patients with Alzheimer's disease: a DARTEL-based voxel-based morphometry study.

Authors:  Chung-Man Moon; Il-Seon Shin; Gwang-Woo Jeong
Journal:  Acta Radiol       Date:  2016-09-30       Impact factor: 1.990

Review 2.  Alzheimer's disease.

Authors:  C A Lane; J Hardy; J M Schott
Journal:  Eur J Neurol       Date:  2017-10-19       Impact factor: 6.089

3.  Gene Expressions, Hippocampal Volume Loss, and MMSE Scores in Computation of Progression and Pharmacologic Therapy Effects for Alzheimer's Disease.

Authors:  Aydin Saribudak; Adarsha A Subick; Na Hyun Kim; Joshua A Rutta; M Umit Uyar
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2018-09-14       Impact factor: 3.710

4.  Regional brain atrophy predicts time to conversion to Alzheimer's disease, dependent on baseline volume.

Authors:  Hossein Tabatabaei-Jafari; Marnie E Shaw; Erin Walsh; Nicolas Cherbuin
Journal:  Neurobiol Aging       Date:  2019-09-10       Impact factor: 4.673

5.  Patterns of Cerebellar Gray Matter Atrophy Across Alzheimer's Disease Progression.

Authors:  Sofia Toniolo; Laura Serra; Giusy Olivito; Camillo Marra; Marco Bozzali; Mara Cercignani
Journal:  Front Cell Neurosci       Date:  2018-11-20       Impact factor: 5.505

  5 in total

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