Clinical applications of resting state functional connectivity

Michael D Fox, Michael Greicius, Michael D Fox, Michael Greicius

Abstract

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm.

Keywords: brain; fMRI; fcMRI; intrinsic activity; neurological disease; psychiatric disease; spontaneous activity.

Figures

Figure 1
Figure 1
Resting state functional connectivity reveals correlations and anticorrelations with the default mode network. Correlations between a seed region in the posterior cingulate/precuneus (PCC) and all other voxels in the brain for a single subject during resting fixation. Both correlations (positive values) and anticorrelations (negative values) are shown, thresholded at R = 0.3. The time course for a single run is shown for the seed region (PCC, yellow), a region positively correlated with this seed region in the medial prefrontal cortex (MPF, orange), and a region negatively correlated with the seed region in the intraparietal sulcus (IPS, blue). Reproduced with permission from (Fox et al., 2005).
Figure 2
Figure 2
Signal to noise features of spontaneous and task evoked activity. (A) fMRI time course from the left somatomotor cortex (LMC) during a single run when the subject pressed the button once with his right hand. Due to poor signal to noise, it is impossible to identify the task-related activity. (B) Comparison of the LMC with the right somatomotor cortex (RMC) shows that much of the noise is ongoing spontaneous activity correlated within the somatomotor system. (C) After subtracting the RMC from the LMC, the task-related modulation from the individual button press is evident (orange arrow). The LMC and RMC regions of interest are displayed for convenience on the inset map. Data taken from (Fox et al., 2006b).
Figure 3
Figure 3
Moving towards resting state abnormalities as a diagnostic marker in Alzheimers: Using parameters derived from resting state functional connectivity and choosing an appropriate threshold one can show good segregation between patients with Alzheimers disease (AD) and healthy elderly (A). Instead of picking just one threshold, receiver operating characteristic (ROC) curves can show the sensitivity and specificity at several different thresholds (B,C). Below each figure are the sensitivity and specificity values obtained by choosing the ideal threshold to segregate the populations in each study. Adapted with permission from (Li et al., ; Greicius et al., ; Supekar et al., 2008).
Figure 4
Figure 4
Resting state fcMRI in pre-operative brain mapping: (A) Structural MRI scan showing a mass in the right frontal cortex. Green circle represents the location of ipsilateral hand response to intra-operative cortical stimulation. (B) Task-related mapping showing activity within the sensorimotor network but also small responses in parietal cortex that are seemingly unrelated to motor function or sensation. (C) Resting-state correlation mapping showing that the sensorimotor network is largely unaffected by the tumor anterior to the central sulcus. Seed region is shown (blue circle). All images are displayed left-on-left. Adapted with permission from (Zhang et al., 2009a).

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