Post by Stephanie Williams
What's the science?
In some types of neuroimaging analyses, a brain-wide signal called the ‘global signal’ can be extracted from functional magnetic resonance (fMRI) scans, a type of scan that measures brain activity indirectly by measuring blood oxygenation/flow. Usually, this signal is identified by averaging the signals from each cortical region or location, at each timepoint in the scan. The information contained in this signal is not completely understood, but different physiological fluctuations contribute to the signal; for example, the signal can be related to heartbeat or to a person moving their body while in the scanner, suggesting some of the signal might represent noise and not true brain activity. Because of this, sometimes scientists have previously removed this signal from their data before continuing with further analysis. However, due to recent findings regarding the composition of the global signal, the practice of removing the signal from data is now less common in neuroimaging research. This week in Nature Scientific Reports , Li and colleagues show that the global signal is related to the cortex in a topographically-specific manner and that it contains information related to trait-level cognition and behavior.
How did they do it?
The authors were interested in understanding two broad aspects of the global signal: 1) How individual differences in the global signal are related cognition and behavior and 2) How local activity fluctuations (in one brain region) are correlated with global signal fluctuations, and how this relationship varies across different cortical regions ( i.e. the “topography” of the global signal). The authors used a cohort (N=1094) of healthy young adults (ages 22-37) from the HCP S1200 dataset. Each participant had several 14.4-minute fMRI scans. During the scans, subjects were not asked to perform any specific task — they were simply resting in the scanner. To extract the global signal for each participant, the authors averaged the signals across all grey matter cortical vertices (known as voxels). To create global signal maps, the authors regressed the global signal across each voxel’s signal fluctuations. This step produced a map of beta coefficients across the cortex, with each beta coefficient representing how similar that local time series was to the averaged time series of the whole brain (a time-dependent correlation).
To extract a general global signal map for all subjects, the authors grouped subjects and averaged across each subject’s average signal map. They also looked at differences between individual subjects (standard deviation of the general global signal map) and looked at how these differences varied across the brain. Next, they analyzed the relationship between the global signal and a series of behavioral measures. They extracted 100 principal components from a pool of behavioral measures and 100 principle components from the global signal for each subject. They then used a machine learning algorithm called canonical correlation analysis to estimate linear combinations of the global signal and behavioral measure components. They used several outputs from this analysis to extract significant brain-brain-behavior associations. They also confirmed that their findings were not confounded by subject motion while in the scanner.
What did they find?
The authors found that 1) global signal is related to local cortical fluctuations in a reliable manner. The topographic maps of the global signal showed strong mapping (high correlations between the local and global time series) in the medial posterior occipital lobes, posterior insula, and central sulcus across subjects. The authors report that the greatest variation in the global signal beta maps arose in visual cortex and retrosplenial cortex. The results of the principal component analysis showed that the first 3 principal components of the global signal were similar to 1) frontoparietal control network 2) default and dorsal attention networks and 3) sensorimotor and visual networks. The spatial patterns of the 3 first principal components were similar to the typical pattern of each of the three networks. The results of the canonical correlation analysis suggest that several significant brain-behavior relationships ( “canonical-variate pairs”) existed and could be inferred from the global signal. The authors found that greater weights for the first global signal principal component--which the authors interpret as the frontoparietal control network-- were related to higher scores in picture vocabulary, temporal discounting, life satisfaction, and lower aggressive antisocial behavior. The authors interpret their findings, supported by previous literature, as showing that brain-behavior relationships can be derived from information contained in the global signal alone.
What's the impact?
The authors have provided evidence for the importance of the global signal, which will affect how individual researchers decide to preprocess their fMRI data. They show that the global signal contains both noise and behaviorally-relevant signals, and should not be removed prior to data analysis.
Li et al. Topography and behavioral relevance of the global signal in the human brain. Scientific Reports (2019). Access the original scientific publication here.