Assessment of Mild Cognitive Impairment in Elderly Subjects Using a Fully Automated Brain Segmentation Software
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (36)
- Petersen RC, "Mild cognitive impairment", Continuum (Minneap Minn), 22, pp.404~418 (2016)
- Jongsiriyanyong S, "Mild cognitive impairment in clinical practice : a review article", Am J Alzheimers Dis Other Demen, 33, pp.500~507 (2018)
- Rathakrishnan BG, "Science to practice : translating automated brain MRI volumetry in Alzheimer's disease from research to routine diagnostic use in the work-up of dementia", Front Neurol, 4, pp.216~ (2014)
- Braak E, "Neuropathology of Alzheimer's disease: what is new since A. Alzheimer?", Eur Arch Psychiatry Clin Neurosci, 249(Suppl 3), pp.14~22 (1999)
- Killiany RJ, "MRI measures of entorhinal cortex vs hippocampus in preclinical AD", Neurology, 58, pp.1188~1196 (2002)
- Bondi MW, "Alzheimer's disease : past, present, and future", J Int Neuropsychol Soc, 23, pp.818~831 (2017)
- Lane CA, "Alzheimer's disease", Eur J Neurol, 25, pp.59~70 (2018)
- Ballard C, "Alzheimer's disease", Lancet, 377, pp.1019~1031 (2011)
- Whitwell JL, "Progression of atrophy in Alzheimer's disease and related disorders", Neurotox Res, 18, pp.339~346 (2010)
- Whitwell JL, "MRI correlates of neurofibrillary tangle pathology at autopsy : a voxelbased morphometry study", Neurology, 71, pp.743~749 (2008)
- Scheltens P, "Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging : interobserver reliability", J Neurol, 242, pp.557~560 (1995)
- Pasquier F, "Inter-and intraobserver reproducibility of cerebral atrophy assessment on MRI scans with hemispheric infarcts", Eur Neurol, 36, pp.268~272 (1996)
- Cavallin L, "Overtime reliability of medial temporal lobe atrophy rating in a clinical setting", Acta Radiol, 53, pp.318~323 (2012)
- Kang KM, "Prediction of amyloid positivity in mild cognitive impairment using fully automated brain segmentation software", Neuropsychiatr Dis Treat, 16, pp.1745~1754 (2020)
- Ochs AL, "Comparison of automated brain volume measures obtained with NeuroQuant and FreeSurfer", J Neuroimaging, 25, pp.721~727 (2015)
- Chung JH, "Scoliosis after pectus excavatum correction : does it improve or worsen", Eur J Cardiothorac Surg, 52, pp.76~82 (2017)
- 변민수, "Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease: Methodology and Baseline Sample Characteristics", PSYCHIATRY INVESTIGATION, 14(6), pp.851~863 (2017), 대한신경정신의학회[DOI]
- Brewer JB, "Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease", AJNR Am J Neuroradiol, 30, pp.578~580 (2009)
- Carter JV, "ROC-ing along : evaluation and interpretation of receiver operating characteristic curves", Surgery, 159, pp.1638~1645 (2016)
- Mueller SG, "Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease", Hum Brain Mapp, 31, pp.1339~1347 (2010)
- Visser PJ, "Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment", J Neurol, 246, pp.477~485 (1999)
- Pini L, "Brain atrophy in Alzheimer's disease and aging", Ageing Res Rev, 30, pp.25~48 (2016)
- Persson K, "Comparison of automated volumetry of the hippocampus using NeuroQuant(R)and visual assessment of the medial temporal lobe in Alzheimer's disease", Acta Radiol, 59, pp.997~1001 (2018)
- Ferrari BL, "The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools", Medicine (Baltimore), 98, pp.e17824~ (2019)
- England HB, "RBANS memory indices are related to medial temporal lobe volumetrics in healthy older adults and those with mild cognitive impairment", Arch Clin Neuropsychol, 29, pp.322~328 (2014)
- Tanpitukpongse TP, "Predictive utility of marketed volumetric software tools in subjects at risk for Alzheimer disease : do regions outside the hippocampus matter", AJNR Am J Neuroradiol, 38, pp.546~552 (2017)
- Kulason S, "Entorhinal and transentorhinal atrophy in preclinical Alzheimer's disease", Front Neurosci, 14, pp.804~ (2020)
- Tabatabaei-Jafari H, "Cerebral atrophy in mild cognitive impairment : a systematic review with meta-analysis", Alzheimers Dement (Amst), 1, pp.487~504 (2015)
- Sluimer JD, "Accelerating regional atrophy rates in the progression from normal aging to Alzheimer's disease", Eur Radiol, 19, pp.2826~2833 (2009)
- Wang PN, "Accelerated hippocampal atrophy rates in stable and progressive amnestic mild cognitive impairment", Psychiatry Res, 171, pp.221~231 (2009)
- Apostolova LG, "Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment(MCI), and Alzheimer disease", Alzheimer Dis Assoc Disord, 26, pp.17~27 (2012)
- Nestor SM, "Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database", Brain, 131, pp.2443~2454 (2008)
- Chou YY, "Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls", Neuroimage, 46, pp.394~410 (2009)
- Min J, "Diagnostic efficacy of structural MRI in patients with mildto-moderate Alzheimer disease : automated volumetric assessment versus visual assessment", AJR Am J Roentgenol, 208, pp.617~623 (2017)
- Bartos A, "Brain volumes and their ratios in Alzheimer's disease on magnetic resonance imaging segmented using Freesurfer 6.0", Psychiatry Res Neuroimaging, 287, pp.70~74 (2019)
- Chee MW, "Brain structure in young and old East Asians and Westerners : comparisons of structural volume and cortical thickness", J Cogn Neurosci, 23, pp.1065~1079 (2011)