Global signal regression strengthens association between resting-state functional connectivity and behavior

Li, Jingwei; Kong, Ru; Liégeois, Raphaël; Orban, Csaba; Tan, Yanrui; Sun, Nanbo; Holmes, Avram J.; Sabuncu, Mert R.; Ge, Tian; Yeo, B. T. Thomas

doi:10.1016/j.neuroimage.2019.04.016

Cited by 281 publications

(267 citation statements)

References 105 publications

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“…In the supplemental material (Section S2), we repeated some analyses on resting-state data that included global signal regression as part of the preprocessing pipeline. Although there is no consensus in the field whether or not the global mean should be eliminated, some work has reported that removal strengthens the association between resting-state functional connectivity and behavior [20,23].…”

Section: Preprocessingmentioning

confidence: 99%

“…We repeated our analysis by including global signal regression (GSR) in the preprocessing pipeline for resting-state data [20,23]. The use of GSR is still debated [26] and can potentially spread underlying group differences to regions that may never have had any [31].…”

Section: S2 Effect Of Global Signal Regression On Identificationmentioning

confidence: 99%

“…We limit our analysis in this section to resting-state data; we did not include GSR in the preprocessing pipeline for results in the main text. In the data employed (see Acknowledgements), 8 subjects' data were removed because they did not not pass quality control check [23]. Thus, the results reported this section were based on N = 92 participants.…”

Section: S2 Effect Of Global Signal Regression On Identificationmentioning

confidence: 99%

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Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification

Venkatesh

JáJá

Pessoa

2019

Preprint

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Understanding the correlation structure associated with multiple brain measurements informs about potential "functional groupings" and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called "fingerprinting") understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). Here, we propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participant's functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data, and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.

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Section: Preprocessingmentioning

confidence: 99%

Section: S2 Effect Of Global Signal Regression On Identificationmentioning

confidence: 99%

Section: S2 Effect Of Global Signal Regression On Identificationmentioning

confidence: 99%

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Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification

Venkatesh

JáJá

Pessoa

2019

Preprint

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“…A recent study used resting state functional connectivity data from HCP dataset to make predictions about behavioural function [21]. The study identified that using functional connectivity with global signal regression improves prediction of a wide range of behavioural phenotypes compared to functional connectivity without global signal regression.…”

Section: Limitationsmentioning

confidence: 99%

Sex classification using long-range temporal dependence of resting-state functional MRI time series

Dhamala

Jamison

Sabuncu

et al. 2019

Preprint

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A thorough understanding of sex differences, if any, that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit differences in clinical and behavioural phenotypes between males and females. In this work, we evaluate sex differences in regional temporal dependence of resting-state brain activity using 195 male-female pairs (aged 22-37) from the Human Connectome Project. Malefemale pairs are strictly matched for total grey matter volume. We find that males have more persistent long-range temporal dependence than females in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies of up to 81%.Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, frontal cortex, and occipital cortex. Additionally, we find that even after males and females are strictly matched on total grey matter volume, significant regional volumetric sex differences persist in many cortical and subcortical regions. Our results indicate males have larger cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger cingulates, precunei, frontal cortices, and parietal cortices. Sex classification based on regional volume achieves accuracies of up to 85%; cerebellum, cingulate cortex, and temporal cortex are the most important features. These findings highlight the important role of strict volume matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between males and females identifies a potential neurobiological substrate underlying sex differences in functional brain activation patterns and the behaviours with which they correlate.

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“…In Qin et al (2019) and Kashyap et al (2019), they decompose RSFC (or timecourse) to extract individual-specific RSFC (or timecourse) with different methods, which obtained obvious improvements in prediction. A second way is to combine various kinds of information with RSFC to enhance the prediction, such as task-fMRI based FC (Elliott et al, 2019;Gao et al, 2019;Xiao et al, 2019) and dynamic FC (Liegeois et al, 2019;Lim et al, 2018;Park et al, 2018), which can provide complementary information to the conventional FC. A third way is to decrease the influence of the possible noise in rs-fMRI signal, for instance, global signal regression and motion artifact correction (Nielsen et al, 2019) have been reported to advance the RSFC-behavior prediction.…”

Section: Bootstrapping Enhanced the Rsfc-phenotype Associationsmentioning

confidence: 99%

Bootstrapping promotes the RSFC-behavior associations: an application of individual cognitive traits prediction

Wei

Jing

2019

Preprint

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Resting state functional connectivity records enormous functional interaction information between any pair of brain nodes, which enriches the prediction of individual phenotypes. To reduce the high dimensional features in prediction, correlation analysis is a common way for feature selection. However, rs-fMRI signal exhibits typically low signal-to-noise ratio and correlation analysis is sensitive to outliers and data distribution, which may bring unstable and uninformative features to subsequent prediction. To alleviate this problem, a bootstrapping-based feature selection framework was proposed and applied on three widely used regression models: connectome-based predictive model (CPM), support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO). A large open-source dataset from Human Connectome Project (HCP) was adopted in the study and a series of cognitive traits were acted as the prediction targets. To systematically investigate the influences of different parameter settings on the bootstrapping-based framework, a total of 216 parameter combinations were evaluated through the R value between the predicted and real cognitive traits, and the best identified performance among them was chosen out as the final prediction accuracy for each cognitive trait. By using bootstrapping without replacement, the best performances of CPM with positive and negative feature sets, SVR and LASSO averagely increased by 28.0%, 33.2%, 11.6% and 24.3% in R values in contrast to the baseline method without bootstrapping. By using bootstrapping with replacement, these best performances increased by 22.1%, 22.9%, 9.4% and 19.6%. Furthermore, the bootstrapping-based feature selection methods could effectively refine the original feature sets obtained from correlation analysis, which thus retained the more stable and informative feature sets. The results demonstrate that bootstrapping-based feature selection is an easy-to-use and effective method to improve RSFC prediction of cognitive traits and is highly recommended in future RSFC prediction studies.

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Global signal regression strengthens association between resting-state functional connectivity and behavior

Cited by 281 publications

References 105 publications

Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification

Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification

Sex classification using long-range temporal dependence of resting-state functional MRI time series

Bootstrapping promotes the RSFC-behavior associations: an application of individual cognitive traits prediction

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