مشخصات مقاله:
عنوان فارسی مقاله:
توصیف مشخصه حفظ وزن در شبکه های پیچیده کارکردی مغزی
عنوان انگلیسی مقاله:
Weight-conserving characterization of complex functional brain networks
مناسب برای رشته های دانشگاهی زیر:
پزشکی
مناسب برای گرایش های دانشگاهی زیر:
مغز و اعصاب – روانپزشکی
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Outline
Abstract
Keywords
Introduction
Methods
Results
Discussion
Acknowledgments
References
قسمتی از مقاله انگلیسی:
Abstract
Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.
Introduction
Large-scale functional brain networks are networks of brain regions and functional connections – coactivations or correlations – between pairs of these regions. Complex functional brain networks are large and extensive networks of nontrivially interacting brain regions that often serve as maps of global brain activity (Bullmore and Sporns, 2009). Interactions between regions in complex functional networks vary in magnitude from large to small, and vary in sign from positive to negative. In contrast, the more traditional “simple” functional networks are smaller groupings of strongly and mutually correlated regions that often serve as maps of specialized functional systems (Fox and Raichle, 2007). Simple functional networks form highly connected modules – components or subnetworks – within complex functional networks (Meunier et al., 2010).