کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5631315 1580864 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Community detection in weighted brain connectivity networks beyond the resolution limit
ترجمه فارسی عنوان
تشخیص جامعه در شبکه های ارتباطی مغز ماورای فراتر از حد قطعنامه
کلمات کلیدی
شبکه های مغز، مدولار، تشخیص جامعه، اتصال به عملکرد شگفت انگیز
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
چکیده انگلیسی


- Methods to study modularity of brain connectivity networks have a resolution limit.
- Asymptotical Surprise, a nearly resolution-limit-free method for weighted graphs, is proposed.
- Improved sensitivity and specificity are demonstrated in model networks.
- Resting state functional connectivity networks consist of heterogeneous modules.
- Classification of hubs in function connectivity networks should be revised.

Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 146, 1 February 2017, Pages 28-39
نویسندگان
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