![]() ![]() ![]() Measures of FC are agnostic regarding causality or direction of connections.įC analyses were first performed on human brain functional data using positron emission tomography (PET), and have since expanded into functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), Electroencephalography (EEG), and peripheral physiological measures. Functional connectivity (FC), in particular, is defined as the statistical association or dependency among two or more anatomically distinct time-series. ![]() The attempt to identify and quantify such inter-regional relationships has been termed “connectivity” analysis. The brain forms a distributed network, whereby specialized regions communicate with each other to process information. Though all implemented measures demonstrate functional connectivity between dACC and DLPFC activity during event-related tasks, different participants appeared to display qualitatively different relationships. 2003 2007) in which activity in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) was evaluated in 32 control subjects during a digit sorting task. We also apply these measures to a previously published fMRI data set ( Siegle et al. We evaluate these measures via simulations with different patterns of functional connectivity and provide recommendations for their use. These measures are categorized into two groups: whole time-series and trial-based approaches. We have implemented a variety of these functional connectivity measures in a new freely-available MATLAB toolbox. Yet these relationships have been quantified using very different measures and the extent to which they index the same constructs is unclear. Functional connectivity has been extensively evaluated with several functional neuroimaging methods, particularly PET and fMRI. The term “functional connectivity” is used to denote correlations in activation among spatially-distinct brain regions, either in a resting state or when processing external stimuli. ![]()
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