| Autor: |
Simpson SL; Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.; Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA., Shappell HM; Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.; Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA., Bahrami M; Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.; Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA. |
| Abstrakt: |
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. |
