Resting-state test-retest reliability over different preprocessing steps

Introduction: Resting-state (RS) functional connectivity (FC) analysis has become a widely used method for the investigation of human brain connectivity and pathology. While most of the current applications are based on data-driven analyses, the use of functionally specific, a priori defined networks provided by neuroimaging meta-analyses represent an important alternative to these, as they allow the standardized assessment of connectivity patterns. Neuronal activity as measured by functional MRI is influenced by various nuisance signals including system noise, thermal noise, and noise induced by physiological processes of the participant. The presence of these confounds in turn have an impact on the estimation of functional connectivity. Several methods exist to deal with this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we investigated the test-retest reliability of FC analyses in meta-analytically defined networks after removing confounding noise regressors. Methods: RS-fMRI data of 42 healthy subjects with an average age of 42 ± 20 years were obtained in two sessions with an average time interval of 175 ± 75 days. A seed-based FC analysis was conducted after spatial preprocessing, approach specific confound-regression, and band-pass filtering [0.01-0.08 Hz]. We focused on the effects of various commonly used confound removals in the resting state studies such as PCA de-noising, global mean signal regression, and removal of tissue-class specific mean signals (in particular, white matter (WM) + cerebrospinal fluid (CSF) and WM + CSF + grey matter (GM)) [2,3,4,5,7]. Additionally, we examined GM specific time-series extraction from seed regions. In order to compute the seed based FC, a priori defined networks were analyzed (extended socio-affective default mode [1] and working-memory [6]). Both networks show robust within network resting state connectivity as well as anti-correlation between each other. The reliability of FC was measured using two different measures Spearman correlations and the absolute differences of functional connectivity scores. The different approaches defined by the combination of different masking / confound removal approaches were compared using a non-parametric Friedman ANOVA. Results: The summary ranking across both indices of reliability (Spearman correlations and absolute differences) reflects the major patterns noted in the individual analyses (Fig.1). GM masking, in particular using the group-mean segmentation, improves reliability. PCA denoising in turn reduces it. Within-network connections are most reliably estimated when not using any global or tissue-class specific signal regression, with removing the global WM and CSF signals representing the next-best approach. In contrast, between-network connections are most reliably measured by linear and second order removal of global signals of all three-tissue classes. Conclusion: Our results show that GM masking of the seed regions based on the group-average GM probabilities is advisable when investigating meta-analytically defined networks. In turn, PCA de-noising reduces the reliability of connectivity estimates. Finally, with respect to global signal regression, we observe that refraining from this approach enhances reliability, but comes at the expense of potentially poorer biological validity, indicated by missing anti-correlations between what has been previously described as antagonistic networks. Here, removal of global WM and CSF signals seems to provide a good compromise, as this approach yielded reliable and meaningful estimates of within and between-network connections (Fig.2). We noted that reliability is proportional to the retained variance, presumably including structured noise. Consequently, we would argue that compromises are needed between maximizing reliability and removing variance that may be attributable to non-neuronal sources

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test–retest reliability of functional connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test–retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test–retest reliability and removing variance that may be attributable to non-neuronal sources

Predicting personality from network-based resting-state functional connectivity

Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC–personality relations should not be considered independently of gender