Dataset Information

The significance of negative correlations in brain connectivity.

ABSTRACT: Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.

SUBMITTER: Zhan L

PROVIDER: S-EPMC6625529 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The significance of negative correlations in brain connectivity.

Zhan Liang L Jenkins Lisanne M LM Wolfson Ouri E OE GadElkarim Johnson Jonaris JJ Nocito Kevin K Thompson Paul M PM Ajilore Olusola A OA Chung Moo K MK Leow Alex D AD

The Journal of comparative neurology 20170716 15

Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, w ...[more]

PMID: 28675490

Similar Datasets

Project description:Resting-state functional magnetic resonance imaging allows one to study brain functional connectivity, partly motivated by evidence that patients with complex disorders, such as Alzheimer's disease, may have altered functional brain connectivity patterns as compared with healthy subjects. A functional connectivity network describes statistical associations of the neural activities among distinct and distant brain regions. Recently, there is a major interest in group-level functional network analysis; however, there is a relative lack of studies on statistical inference, such as significance testing for group comparisons. In particular, it is still debatable which statistic should be used to measure pairwise associations as the connectivity weights. Many functional connectivity studies have used either (full or marginal) correlations or partial correlations for pairwise associations. This article investigates the performance of using either correlations or partial correlations for testing group differences in brain connectivity, and how sparsity levels and topological structures of the connectivity would influence statistical power to detect group differences. Our results suggest that, in general, testing group differences in networks deviates from estimating networks. For example, high regularization in both covariance matrices and precision matrices may lead to higher statistical power; in particular, optimally selected regularization (e.g., by cross-validation or even at the true sparsity level) on the precision matrices with small estimation errors may have low power. Most importantly, and perhaps surprisingly, using either correlations or partial correlations may give very different testing results, depending on which of the covariance matrices and the precision matrices are sparse. Specifically, if the precision matrices are sparse, presumably and arguably a reasonable assumption, then using correlations often yields much higher powered and more stable testing results than using partial correlations; the conclusion is reversed if the covariance matrices, not the precision matrices, are sparse. These results may have useful implications to future studies on testing functional connectivity differences.

Project description:Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or "brain-read", unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90-100%) were achieved with 15-40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction.

Dataset Information

The significance of negative correlations in brain connectivity.

Publications

The significance of negative correlations in brain connectivity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets