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Inference on Multi-level Partial Correlations Based on Multi-subject Time Series Data
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DOI：10.1080/01621459.2021.1917417.png)
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摘要
En 中文
Partial correlations are commonly used to analyze the conditional dependence among variables. In this work, we propose a hierarchical model to study both the subject- and population-level partial correlations based on multi-subject time-series data. Multiple testing procedures adaptive to temporally dependent data with false discovery proportion control are proposed to identify the nonzero partial correlations in both the subject and population levels. A computationally feasible algorithm is developed. Theoretical results and simulation studies demonstrate the good properties of the proposed procedures. We illustrate the application of the proposed methods in a real example of brain connectivity on fMRI data from normal healthy persons and patients with Parkinson's disease. Supplementary materials for this article are available online.
Keyword:
Brain functional connectivity
fMRI
High dimensionality
Partial correlation
Time-dependent data
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5.2K
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