数据科学
领域(数学)
计算机科学
网络分析
马尔可夫链
网络科学
统计模型
人工智能
复杂网络
机器学习
万维网
数学
量子力学
物理
纯数学
作者
Sean L. Simpson,Heather Shappell,Mohsen Bahrami
出处
期刊:Annual review of statistics and its application
[Annual Reviews]
日期:2023-11-27
卷期号:11 (1)
标识
DOI:10.1146/annurev-statistics-040522-020722
摘要
The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks—a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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