Consistent resting-state networks across healthy subjects

Abstract

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

Conflict of interest statement

Conflict of interest statement: No conflicts declared.

Figures

Fig. 1.

Tensor-PICA estimated resting patterns of the first (A–J) and second (A′–I′ and K) multisubject data sets: coronal, sagittal, and axial view of spatial map for each component. A–I and A′–I′ show components found in both data sets. J and K components are unique to their data sets. Images are z statistics overlaid on the average high-resolution scan transformed into standard (MNI152) space. Black to yellow are z values, ranging from 2.0 to 5.0. The left hemisphere of the brain corresponds to the right side of the image.

Fig. 2.

Mean (across 100 surrogate multisubject data sets) tensor-PICA estimated resting patterns: coronal, sagittal, and axial view of spatial map for each component. Images are percentage BOLD signal change, overlaid on the average high-resolution scan transformed into standard (MNI152) space. Black to yellow is percentage signal change, ranging from 0.5% to 3.0%.

Fig. 3.

Maps of coefficient of variation: coronal, sagittal, and axial view of spatial map for each component. Images are percentage variation around the mean percentage BOLD signal change, overlaid on the average high-resolution scan transformed into standard (MNI152) space. Red denotes much variation around the mean; blue denotes little variation.