User:Mac Merritt/Dynamic Functional Connectivity

Dynamic Functional Connectivity refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic Functional Connectivity has been observed with several mediums but the primary one has been fMRI. DFC is a recent discovery within the field of functional neuroimaging. The discovery of DFC was motivated by the observation of temporal variability in the rising field of steady state connectivity research

static connectivity
Functional connectivity refers to the functionally integrated relationship between spatially separated brain regions. Unlike structural connectivity which looks for physical connections in the brain, functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions .. This type of connectivity was discovered in the mid 1990s and has been seen primarily using fMRI and Positron emission tomography. 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. 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 short time over a task or period of data collection.

The origin of dynamic analysis
Studies that showed brain state dependent changes in functional connectivity were the first indicators that temporal variation in functional connectivity may be significant. Several Studies in the mid 2000s examined the changes in FC that were related to a variety of different causes such as mental tasks, sleep , and learning. These changes often occur within the same individual and are difficult to explain by one to one structural changes which tend to occur on a slower time scale. This observation lead to an increased focus on the dynamic nature of functional connectivity from the research community. DFC has now been investigated in a variety of different contexts with many analysis tools. It has been shown to be related to both behavior and neural activity, and some researchers believe that it may be heavily related to high level thought or consciousness.

Significant findings from DFC
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 there implications; however many critical findings have been made that help the scientific community to better understand the brain. Traditional neuroscience often depicts that brain networks are static in time and relatively long lasting. Analysis of dynamic functional connectivity has shown that far from being completely static, the functional networks of the brain fluctuate on the scale of seconds to minutes. These changes are generally seen as movements from one short term state to another rather than continuous shifts. Many studies have shown reproducible patterns of network activity that moves through out the brain. These patterns have been seen in both animals and humans, and are present at only certain points during a scanner session. In addition to showing transient brain states, DFC analysis has shown a distinct organization of the networks of the brain. Connectivity between bilaterally symmetric regions is the most stable form of connectivity in the brain, followed by other regions with direct anatomical connections. Steady state functional connectivity networks exist and have physiological relevance, but have less temporal stability than the anatomical networks. Networks also exist that are fleeting enough to only be seen with DFC analysis. These networks also possess physiological relevance but are much less temporally stable than the other networks in the brain.

Sliding Window
Sliding window analysis is the most common method used in the analysis of functional connectivity. Sliding window analysis is performed by conducting analysis on a set number of scans in an fMRI session. The number of scans is the length of the sliding window. The defined window is then moved a certain number of scans forward in time and additional analysis is performed. The movement of the window is usually referenced in terms of the degree of overlap between adjacent windows. One of the principle benefits of sliding window analysis is that almost any steady state analysis can also be performed using sliding window if the window length is sufficiently large. Sliding window analysis also has a benefit of being easy to understand and in some ways easier to interpret. As the most common method of analysis, sliding window analysis has been used in many different ways to investigate a variety of different characteristics and implications of DFC. 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.

Activation Patterns
One of the first methods ever used to analyze DFC used pattern analysis of fMRI images to show that there are patterns of activation in spatially separated brain regions that tend to activity together. It has become clear that there is a spatial and temporal periodicity in the brain that probably reflects some of the constant processes of the brain. Repeating patterns of network information has been suggested to account for 25-50% of the variance in fMRI BOLD data. These patterns of activity have primarily been seen primarily in rats as a propagating wave of synchronized activity along the cortex of the rat. These waves have also been shown to be related to underling neural activity, and has been shown to be present in humans as well as rats.

Other Methods
Time frequency analysis has been proposed as an analysis method that is capable of overcoming many of the challenges associated with sliding window analysis. Unlike sliding window analysis, time frequency analysis allows the researcher to investigate both frequency and amplitude information simultaneously. The wavlet transform has been used to conduct DFC analysis that has validated the existence of DFC by showing its significant changes in time. This same method has recently been used to investigate some of the dynamic characteristics of accepted networks. For example, time frequency analysis has shown that the anticorrelation between the default mode network and the task positive network is not constant in time but rather is a temporary state. Independent component analysis has become one of the most common methods of network generation in steady state functional connectivity. ICA divides fMRI signal into several spatial components that have similar temporal patterns. More recently, ICA has been used to divide fMRI data into different temporal components. 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.

Controversy and limitations
Several researchers have argued that DFC is a simple reflection of analysis, scanner, or physiological noise. Noise in fMRI can arise from a variety of different factors including heart beat, changes in the blood brain barrier, characterize of the acquiring scanner, and unattained effects of analysis. Some researchers have proposed that the variability in functional connectivity in fMRI studies is consistent with the variability that one would expect from simply analyzing random data. This complaint that DFC may reflect only noise has been recently lessened by the observation of electrical similarity to fMRI data and behavioral relevance of DFC characteristics.

Physiological Evidence
fMRI is the primary means of investigating DFC. This presents unique challenges because fMRI has fairly low temporal resolution (.5 Hz) and is only an indirect measure of neural activity. The indirect nature of fMRI analysis suggests that validation is needed to show that findings from fMRI are actually relevant and reflective of neural activity.

Electrophysiology
Correlation between DFC and electrophysiology has lead some scientists to suggest that DFC could reflect hemodynamic results of dynamic network behavior that has been seen in single cell analysis of neuron populations. Although hemodynamic response is too slow to reflect a one to one correspondence with network dynamics, it is plausible that DFC is a reflection of the power of some frequencies of electrophysiology data EEG has also been used in humans to both validate and interpret observations made in DFC. 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. EEG has been used simultaneously with fMRI to account for some of the inter scan variance in in FC. EEG has also been used to show that changes in FC are related broad brain states observed in EEG.

MEG
MEG can be used to measure the magnetic fields produced by electrical activity in the brain. MEG has high temporal resolution and has generally higher spatial resolution than EEG. Resting state studies with MEG are still limited by spatial resolution, but the modality has been used to show that resting state networks move through periods of low and high levels of correlation. This observation is consistent with the results seen in other DFC studies such as DFC activation pattern analysis.

Behavioral Basis
DFC has been shown to be significantly related to performance and specifically vigilance. It has been proposed and supported that the network behavior immediately prior to a task onset is a strong predictor of performance on that task. Traditionally, fMRI studies have focused on the magnitude of activation in brain regions as a predictor of performance, but recent research has shown that correlation between networks as measured with sliding window analysis is an even stronger predictor of performance.

Clinical Relevance
One of the principle motivations of DFC analysis is to better understand detect and treat neurological diseases. Static functional connectivity has been shown to be significantly related to a variety of diseases such as depression, schizophrenia, and Alzheimer's disease. Because of the newness of the field, DFC has only recently been used to investigate disease states, but since 2012 each of these three diseases has been shown to be correlated to changes in dynamic characteristics of Functional connectivity. Most of these differences are related to the amount of time that is spent in different transient states. Patients with Schizophrenia have been studied and revealed to have less frequent state changes than healthy patients. It has been suggested that the diseas is related to paients being stuck in certain brain states where the brain is unable to respond quickly to different queues. Depression and bi plolar disorder have been studied and similiar results were found related to differences in the amount of time that subjects spend in different brain states. Studies with Alzheimers disease have shown that patients suffering from this ailment have altered network connectivity as well as altered time spent in the networks that are present. All of the clinical research that has been conducted using DFC correlation between DFC changes and disease state. This correlation does not imply that the changes in DFC are the cause of any of these diseases, but information from DFC analysis may be used to better understand the effects of the disease and diagnose them.