A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment
المؤلفون المشاركون
Jiang, Jiehui
Zuo, Chuantao
Yan, Zhuangzhi
Xiao, Shu-yun
Wang, Min
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-18
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Objective.
Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically.
However, existing discriminant methods are unsubtle to reveal pathophysiological changes.
Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately.
Methods.
In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up.
Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features.
Three different classification strategies were implemented to interrogate the predictive performance.
To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge.
Results.
As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features.
Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933).
The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus.
Conclusion.
Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD.
The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Min& Yan, Zhuangzhi& Xiao, Shu-yun& Zuo, Chuantao& Jiang, Jiehui. 2020. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural Neurology،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1138374
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Min…[et al.]. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural Neurology No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1138374
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Min& Yan, Zhuangzhi& Xiao, Shu-yun& Zuo, Chuantao& Jiang, Jiehui. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural Neurology. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1138374
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138374
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر