A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference
المؤلفون المشاركون
Li, Junning
McKeown, Martin J.
Wang, Z. Jane
Liu, Aiping
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2012، العدد 2012 (31 ديسمبر/كانون الأول 2012)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2012-11-04
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity.
Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges.
The PCfdr algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically.
The original PCfdr algorithm was unable to accommodate a priori information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects.
Here we extend the original PCfdr algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity.
In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold.
When the proposed approach is applied to fMRI data in a Parkinson’s disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication.
The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Aiping& Li, Junning& Wang, Z. Jane& McKeown, Martin J.. 2012. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-512126
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Aiping…[et al.]. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-14.
https://search.emarefa.net/detail/BIM-512126
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Aiping& Li, Junning& Wang, Z. Jane& McKeown, Martin J.. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-512126
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-512126
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