Appendix - Brain connectivity analyses



Supplemental Digital Content 4:

Brain connectivity analyses

Until recently, most functional magnetic resonance imaging (MRI) studies of brain function were performed during the administration of a task; these studies measured the resulting regional changes in neuronal activity induced by experimental manipulations. However, recent progress has been performed in studying spontaneous brain activity using the so-called ‘resting state’ functional MRI technique. Numerous studies in healthy volunteers1-7 have demonstrated that resting state functional MRI is able to identify coherent activity patterns in functional brain networks which closely agree with those identified during cognitive tasks or sensory stimulation. This technique may show promise for the study of higher order associative network functionality and its potential abnormalities in pathology8-11 or in altered states of consciousness,12-13 when the subjects are unable to perform a task or to communicate.

There are two main ways to analyze resting–state functional connectivity MRI (rs-fMRI): (1) hypothesis-driven seed-voxel4 and (2) data-driven - mainly independent component analysis (ICA) approaches14 – each offering their own advantages and limitations. The seed-voxel approach consists of extracting the blood oxygen level dependent (BOLD) time course from a region of interest and determining the temporal correlation between this signal (seed) and the signal from all other brain voxels.4 To reduce spurious variance unlikely to reflect neuronal activity, the BOLD signal is preprocessed by temporal band-pass filtering, spatial smoothing, and by regressing out of head motion curves, whole brain signal and ventricular and white matter signals.4 This method, which is quite straightforward and gives very intuitive results has been widely adopted and seems to give very consistent results.3 On the other hand, it has raised some controversial issues mostly related to arbitrary choices that have to be performed in the preprocessing procedures,15-17 a potentially suboptimal correction for physiological noise,18 and some potential for user-dependent bias in the choice of the seed-voxels of interest used to compute correlation patterns.

In contrast to seed-voxel approaches, ICA-based analyses14 do not require a priori definition of regions of interest. ICA analyzes the entire BOLD dataset and decomposes it into components that are maximally statistically independent. A number of studies have shown that ICA is a powerful tool which can simultaneously extract a variety of different coherent neuronal networks7,19-22 and separate them from other signal modulations such as those induced by head motion or physiological confounds (e.g., cardiac pulsation, respiratory cycle and slow changes in the depth and rate of breathing.23-25 On the other hand, the interpretation of independent component analysis is sometimes less straightforward (it provides some network-level connectivity quantification, rather than a direct measure of correlation between brain regions) and is less efficient than seed-voxel approaches in detecting some patterns of interest such as between-network anticorrelations. A popular approach is now to combine these connectivity measures in the study of resting state BOLD functional MRI fluctuations.16 Similar results using both approaches provide an additional guarantee that results are not due to the particular analysis method used. Figure 1 in the main manuscript and Supplementary Digital Content 7 explain general principles of seed-voxel based and ICA-based analyses as used in the present study.

Note that rs-fMRI studies assess functional connectivity, i.e., correlation patterns, and not effective connectivity, i.e., causal interactions between distant brain areas. Assessing causal interactions between areas would however require a faster temporal resolution than that of functional MRI. At the same time, functional MRI studies offer the advantage of providing measurements of the whole-brain in the same acquisition, with a spatial resolution allowing precise anatomical localization of the different patterns of correlation. Inferences about effective connectivity from functional neuroimaging studies using positron emission tomography or functional MRI require prior knowledge about anatomical connections between areas.26 This anatomical connectivity has increasingly been shown to underlie functional connectivity in resting state functional MRI network patterns.6,27-28 However, current functional MRI studies of anesthesia-induced changes in brain connectivity will ideally be complemented in the future by effective connectivity studies using higher temporal resolution measurement techniques such as high-density electroencephalography.29

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