Positive Connectivity Predicts the Dynamic Intrinsic Topology of the Human Brain Network

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.

Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. Furthermore, we aimed at clarifying possible causes of anticorrelations and test the hypothesis that anticorrelations are introduced via certain preprocessing approaches, with particular focus on the effects of regression against the global signal. Our results show that preprocessing in general greatly increased connection specificity, in particular correction for global signal fluctuations almost doubled connection specificity. However, widespread anticorrelated networks were only found when regression against the global signal was applied. Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.

We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.