A group analysis using the multiregression dynamic models for fMRI networked time series

DOI10.1016/j.jspi.2018.03.004zbMath1432.62294OpenAlexW2795984945WikidataQ64110653 ScholiaQ64110653MaRDI QIDQ1644426

Lilia Costa, James Q. Smith, Thomas E. Nichols

Publication date: 21 June 2018

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2018.03.004

zbMATH Keywords

cluster analysisgroup analysisBayesian networkfunctional magnetic resonance imaging (fMRI)multiregression dynamic model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biomedical imaging and signal processing (92C55)

Uses Software

• SCIP
• TETRAD
• dynamicTreeCut

Cites Work

• Searching multiregression dynamic models of resting-state fMRI networks using integer programming
• Bayesian forecasting and dynamic models.
• Studying the effective brain connectivity using multiregression dynamic models
• Exact estimation of multiple directed acyclic graphs
• Cluster Analysis
• Handbook of Functional MRI Data Analysis
• Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network
• Toward a Multisubject Analysis of Neural Connectivity
• State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI
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