10.1038/s41598-019-50750-8Scientific Reports911428

Bolt, T.

Bzdok, D.

Li, J.

Nomi, J.S.

Spreng, R.N.

Uddin, L.Q.

Yeo, B.T.T.

Scientific Reports

International audienceThe global signal in resting-state functional MRi data is considered to be dominated by physiological noise and artifacts, yet a growing literature suggests that it also carries information about widespread neural activity. the biological relevance of the global signal remains poorly understood. Applying principal component analysis to a large neuroimaging dataset, we found that individual variation in global signal topography recapitulates well-established patterns of large-scale functional brain networks. Using canonical correlation analysis, we delineated relationships between individual differences in global signal topography and a battery of phenotypes. The first canonical variate of the global signal, resembling the frontoparietal control network, was significantly related to an axis of positive and negative life outcomes and psychological function. these results suggest that the global signal contains a rich source of information related to trait-level cognition and behavior. this work has significant implications for the contentious debate over artifact removal practices in neuroimaging. In the imaging neurosciences, the global signal (GS) is defined as the timeseries of signal intensity averaged across all voxels in the brain, gray matter, or cortical gray matter. It is well known that non-neuronal sources including physiological noise caused by respiratory and cardiac events 1,2 and participant motion 3 contribute to the GS. As a consequence, GS regression became a pervasively adopted step in processing of resting-state fMRI data to attenuate these and other sources of noise 4,5. However, in addition to containing artifactual information from various sources, the GS also contains information about ongoing neural activity 6. Combined fMRI-electrophysiological studies in macaque monkeys permit analysis of spatiotemporal covariation between neural signal fluctuations measured with implanted electrodes and concurrent hemodynamic signals measured with fMRI. In one study, spontaneous fluctuations in local field potentials exhibited widespread positive correlations with fMRI blood oxygen level dependent (BOLD) changes over the entire macaque cortex 7. More recently, neural origins of the global signal were indicated by inactivation of a neuromodulatory region of the basal forebrain, the nucleus basa-lis of Meynert. The nucleus basalis gives rise to the principal cholinergic as well as GABAergic projections to the cortex. Reversible pharmacological inactivation of the nucleus basalis in macaques resulted in regionally specific suppression of the global signal ipsilateral to the injection, further demonstrating a direct neuronal source of the global signal 8. Total estimates of baseline neuronal processing also come from magnetic resonance spectroscopy studies using 13 C radiotracers, which permit simultaneous measures of energy demand (CMR O2) in neurons and glia as well as neuronal activity as reflected by presynaptic release of the neurotransmitters glutamate and GABA 9. Such work in rodents suggests that around 80% of neuronal energy in the cerebral cortex supports global neuronal activity at rest 10. In light of these findings, Hyder and colleagues suggest that neither total baseline neuronal activity nor fluctuations in baseline neuronal activity can be neglected as merely representing non-neuronal factors 9 

Li, Jingwei

Bolt, Taylor

Bzdok, Danilo

Nomi, Jason, S

Thomas Yeo, B, T

Nathan Spreng, R

Uddin, Lucina, Q

HAL-CEA

Topography and Behavioral Relevance of the Global Signal in the Human Brain

The global signal in resting-state functional MRI data is considered to be dominated by physiological noise and artifacts, yet a growing literature suggests that it also carries information about widespread neural activity. The biological relevance of the global signal remains poorly understood. Applying principal component analysis to a large neuroimaging dataset, we found that individual variation in global signal topography recapitulates well-established patterns of large-scale functional brain networks. Using canonical correlation analysis, we delineated relationships between individual differences in global signal topography and a battery of phenotypes. The first canonical variate of the global signal, resembling the frontoparietal control network, was significantly related to an axis of positive and negative life outcomes and psychological function. These results suggest that the global signal contains a rich source of information related to trait-level cognition and behavior. This work has significant implications for the contentious debate over artifact removal practices in neuroimaging

Topography and behavioral relevance of the global signal in the human brain

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