Dataset Information

Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity.

ABSTRACT: The application of global signal regression (GSR) to resting-state functional magnetic resonance imaging data and its usefulness is a widely discussed topic. In this article, we report an observation of segregated distribution of amygdala resting-state functional connectivity (rs-FC) within the fusiform gyrus (FFG) as an effect of GSR in a multi-center-sample of 276 healthy subjects. Specifically, we observed that amygdala rs-FC was distributed within the FFG as distinct anterior versus posterior clusters delineated by positive versus negative rs-FC polarity when GSR was performed. To characterize this effect in more detail, post hoc analyses revealed the following: first, direct overlays of task-functional magnetic resonance imaging derived face sensitive areas and clusters of positive versus negative amygdala rs-FC showed that the positive amygdala rs-FC cluster corresponded best with the fusiform face area, whereas the occipital face area corresponded to the negative amygdala rs-FC cluster. Second, as expected from a hierarchical face perception model, these amygdala rs-FC defined clusters showed differential rs-FC with other regions of the visual stream. Third, dynamic connectivity analyses revealed that these amygdala rs-FC defined clusters also differed in their rs-FC variance across time to the amygdala. Furthermore, subsample analyses of three independent research sites confirmed reliability of the effect of GSR, as revealed by similar patterns of distinct amygdala rs-FC polarity within the FFG. In this article, we discuss the potential of GSR to segregate face sensitive areas within the FFG and furthermore discuss how our results may relate to the functional organization of the face-perception circuit.

SUBMITTER: Kruschwitz JD

PROVIDER: S-EPMC6868984 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity.

Kruschwitz Johann D JD Meyer-Lindenberg Andreas A Veer Ilya M IM Wackerhagen Carolin C Erk Susanne S Mohnke Sebastian S Pöhland Lydia L Haddad Leila L Grimm Oliver O Tost Heike H Romanczuk-Seiferth Nina N Heinz Andreas A Walter Martin M Walter Henrik H

Human brain mapping 20150714 10

The application of global signal regression (GSR) to resting-state functional magnetic resonance imaging data and its usefulness is a widely discussed topic. In this article, we report an observation of segregated distribution of amygdala resting-state functional connectivity (rs-FC) within the fusiform gyrus (FFG) as an effect of GSR in a multi-center-sample of 276 healthy subjects. Specifically, we observed that amygdala rs-FC was distributed within the FFG as distinct anterior versus posterio ...[more]

PMID: 26178527

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Project description:Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

Dataset Information

Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity.

Publications

Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity.

Similar Datasets

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