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Hum Brain Mapp. 2016 Dec;37(12):4566-4580. doi: 10.1002/hbm.23329. Epub 2016 Jul 28.

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

Sojoudi A1,2, Goodyear BG1,2,3,4,5,6.

Author information

Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.
Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada.
Department of Radiology, University of Calgary, Calgary, Alberta, Canada.
Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.


Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016.


Bayesian inference; coefficient of determination; dynamic functional connectivity; fMRI; functional connection variability; hierarchical observation modeling

[Indexed for MEDLINE]

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