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Neuroimage. 2015 Jun;113:310-9. doi: 10.1016/j.neuroimage.2015.03.021. Epub 2015 Mar 19.

A two-part mixed-effects modeling framework for analyzing whole-brain network data.

Author information

1
Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA; Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: slsimpso@wakehealth.edu.
2
Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA.

Abstract

Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function.

KEYWORDS:

Connectivity; Graph theory; Mixed model; Network model; Small-world; fMRI

PMID:
25796135
PMCID:
PMC4433821
DOI:
10.1016/j.neuroimage.2015.03.021
[Indexed for MEDLINE]
Free PMC Article

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