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Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features
المؤلفون المشاركون
Wang, Xin
Ren, Yanshuang
Zhang, Wensheng
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-04-12
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification.
One popular approach to construct FBN is Pearson correlation.
However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions.
Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN.
To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls.
First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions.
Moreover, it can automatically remove weak relationship and retain the modular structure of FBN.
Secondly, FBN are effectively measured by eight graph-based features from different aspects.
Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN.
In addition, the combination of graph-based features in our method further improves classification performance.
Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xin& Ren, Yanshuang& Zhang, Wensheng. 2017. Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1142074
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xin…[et al.]. Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1142074
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xin& Ren, Yanshuang& Zhang, Wensheng. Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1142074
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142074
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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