An Efficient Implementation of the Synchronization Likelihood Algorithm for Functional Connectivity

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• 作者：Francisco Rosales ; Antonio García-Dopico ; Ricardo Bajo ; ángel Nevado
• 关键词：Functional connectivity ; Synchronization likelihood ; Implementation ; Parallelization ; OpenMP ; GPU
• 刊名：Neuroinformatics
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：13
• 期：2
• 页码：245-258
• 全文大小：948 KB
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• 作者单位：Francisco Rosales (1)
  Antonio García-Dopico (1)
  Ricardo Bajo (2)
  ángel Nevado (2) (3)
  
  1. DATSI Computer Science, Polytechnic University of Madrid, Madrid, Spain
  2. Laboratory of Cognitive and Computational Neuroscience Centre for Biomedical Technology, Polytechnic University of Madrid, Madrid, Spain
  3. Basic Psychology Department II, Complutense University of Madrid, Madrid, Spain
  
• 刊物主题：Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology;
• 出版者：Springer US
• ISSN：1559-0089

文摘

Measures of functional connectivity are commonly employed in neuroimaging research. Among the most popular measures is the Synchronization Likelihood which provides a non-linear estimate of the statistical dependencies between the activity time courses of different brain areas. One aspect which has limited a wider use of this algorithm is the fact that it is very computationally and memory demanding. In the present work we propose new implementations and parallelizations of the Synchronization Likelihood algorithm with significantly better performance both in time and in memory use. As a result both the amount of required computational time is reduced by 3 orders of magnitude and the amount of memory needed for calculations is reduced by 2 orders of magnitude. This allows performing analyses that were not feasible before from a computational standpoint.