Detection of functional connectivity using temporal correlations in MR images

Abstract

Functional connectivity among brain regions has been investigated via an analysis of correlations between regional signal fluctuations recorded in magnetic resonance (MR) images obtained in a steady state. In comparison with studies of functional connectivity that utilize task manipulations, the analysis of correlations in steady state data is less susceptible to confounds arising when functionally unrelated brain regions respond in similar ways to changes in task. A new approach to identifying interregional correlations in steady state data makes use of two independent data sets. Regions of interest (ROIs) are defined and hypotheses regarding their connectivity are generated in one data set. The connectivity hypotheses are then evaluated in the remaining (independent) data set by analyzing low frequency temporal correlations between regions. The roles of the two data sets are then reversed and the process repeated, perhaps multiple times. This method was illustrated by application to the language system. The existence of a functional connection between Broca's area and Wernicke's area was confirmed in healthy subjects at rest. An increase in this functional connection when the language system was actively engaged (when subjects were continuously listening to narrative text) was also confirmed. In a second iteration of analyses, a correlation between Broca's area and a region in left premotor cortex was found to be significant at rest and to increase during continuous listening. These findings suggest that the proposed methodology can reveal the presence and strength of functional connections in high-level cognitive systems.

Antidepressant Effect on Connectivity of the Mood-Regulating Circuit: An fMRI Study

“…A number of recent studies have suggested that the brain remains quite active during the so-called unstimulated state (Shulman, 2001), and that steady data variables such as LFBF could provide important knowledge regarding localized brain activity and connectivity (Maldjian, 2001;Shulman, 2001). Recent studies have used steady-state LFBF correlation data to elucidate connectivity in circuits involved in motor movements (Lowe et al, 2000), speech (Hampson et al, 2002), and working memory . Changes in LFBF correlation as a measure of abnormal connectivity have also been reported in disease states such as multiple sclerosis (Lowe et al, 2002), and in brief reports in schizophrenia (Driesen, 2003), depression (Skudlarski et al, 2000), and bipolar disorder (Blumberg, 2003).…”

“…Moreover, functionally unconnected regions may respond similarly to changes associated with a task. These confounds can be avoided to some extent by measuring correlations across brain regions in steady-state data (Shulman, 2001;Hampson et al, 2002). Recent studies have reported, using PET to measure resting blood glucose metabolism, changes in the corticolimbic circuitry after treatment in depressed patients (Mayberg, 2002;Seminowicz, 2004), but until recently, methods were not available to measure steady-state blood flow changes using fMRI.…”

“…It has recently been recognized that these LFBFs (o0.08 Hz) are not caused by instrumentations or physiological effects (such as cardiac and respiratory cycles) originating outside the brain (Biswal et al, 1995a;Cordes et al, 2001), and that these resting state signal changes reflect alterations in blood flow and oxygenation that may be coupled to neuronal activity (Maldjian, 2001). LFBF correlation as a measure of functional connectivity between distant functionally related brain regions has been used to demonstrate connectivity between brain regions, which are known to be functionally related from neurophysiological and neurological studies, for example, DLPFC and middle frontal gyrus during performance of a finger tapping task (Lowe et al, 1998), sensorimotor and language areas (Cordes et al, 2000), Broca's and Wernicke' s area while performance of a speech task (Hampson et al, 2002), and cingulate cortex and prefrontal cortex during performance of a working memory task . Therefore, using fMRI, LFBF correlations may be useful as a measure of connectivity within the MRC.…”

Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data

“…A common measure used to examine functional connectivity is the temporal correlation between brain voxels (Salvador et al, 2005;Hampson et al, 2002;Lowe et al, 1998;Grecius et al, 2003;Xiong et al, 1999). However, correlation in functional neuroimaging data cannot only be attributed to a potential neurophysiologically based association, but also to a host of other factors such as head movement, spatial realignment and normalization, scanner induced correlations, and partial voluming.…”

“…Secondly, the approach is limited in its practical use as it relies on permutation of 2D slices and thus is only able to determine significant connectivity within each slice. Recent connectivity studies (Salvador et al, 2005;Hampson et al, 2002;Lowe et al, 1998;Grecius et al, 2003;Xiong et al, 1999) focus on correlations throughout the entire intra-cranial space in 3D.…”