Dynamic functional connectivity

DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks.

[2] Functional connectivity is usually measured during resting state fMRI and is typically analyzed in terms of correlation, coherence, and spatial grouping based on temporal similarities.

[3] These methods have been used to show that functional connectivity is related to behavior in a variety of different tasks, and that it has a neural basis.

These methods assume the functional connections in the brain remain constant in a short time over a task or period of data collection.

Several studies in the mid-2000s examined the changes in FC that were related to a variety of different causes such as mental tasks,[4] sleep,[5] and learning.

[3] Because DFC is such a new field, much of the research related to it is conducted to validate the relevance of these dynamic changes rather than explore their implications; however, many critical findings have been made that help the scientific community better understand the brain.

In order to be accurately interpreted, data from sliding window analysis generally must be compared between two different groups.

Researchers have used this type of analysis to show different DFC characteristics in diseased and healthy patients, high and low performers on cognitive tasks, and between large scale brain states.

[7] Departing from the traditional approaches, recently an efficient method was introduced to analyze rapidly changing functional activations patterns which transforms the fMRI BOLD data into a point process.

The large information content of these few points is consistent with the results of Petridou et al.[18] who demonstrated he contribution of these "spontaneous events" to the correlation strength and power spectra of the slow spontaneous fluctuations by deconvolving the task hemodynamic response function from the rest data.

[22] Independent component analysis has become one of the most common methods of network generation in steady state functional connectivity.

This has been termed temporal ICA and it has been used to plot network behavior that accounts for 25% of variability in the correlation of anatomical nodes in fMRI.

Noise in fMRI can arise from a variety of different factors including heart beat, changes in the blood brain barrier, characteristics of the acquiring scanner, or unintended effects of analysis.

Because of its indirect nature, fMRI data in DFC studies could be criticized as potentially being a reflection of non neural information.

This concern has been alleviated recently by the observed correlation between fMRI DFC and simultaneously acquired electrophysiology data.

The scientists claim indeed that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity.

This presents unique challenges because fMRI has fairly low temporal resolution, typically 0.5 Hz, and is only an indirect measure of neural activity.

EEG has poor spatial resolution because it is only able to acquire data on the surface of the scalp, but it is reflective of broad electrical activity from many neurons.

[3] Single-unit recording were used in order to explore the extent, strength and plasticity of functional connectivity between individual cortical neurons in cats and monkeys.

[24] Individual differences in functional connectivity variability (FCV) across sliding windows within fMRI scans have been shown to correlate with the tendency to attend to pain.

The default mode network above is one example of a brain network seen using steady state connectivity. This network is fairly stable in time, but it has been shown to have a variable relationship with other networks, and to vary slightly in its own characteristics in time.
Full EEG caps like the one above are often used simultaneously with fMRI in order to capture information about the electrical signals underlying the BOLD signal.