The elusive concept of brain network. Comment on "Understanding brain networks and brain organization" by Luiz Pessoa

Functional resting-state brain connectivity is accompanied by dynamic correlations of application-dependent [18F]FDG PET-tracer fluctuations

Brain function is characterized by a convolution of various biochemical and physiological processes, raising the interest whether resting-state functional connectivity derived from hemodynamic scales shows underlying metabolic synchronies. Increasing evidence suggests that metabolic connectivity based on glucose consumption associated PET recordings may serve as a marker of cognitive functions and neuropathologies. However, to what extent fMRI-derived resting-state brain connectivity can also be characterized based on dynamic fluctuations of glucose metabolism and how metabolic connectivity is influenced by [18F]FDG pharmacokinetics remains unsolved. Simultaneous PET/MRI measurements were performed in a total of 26 healthy male Lewis rats. Simultaneously to resting-state fMRI scans, one cohort (n = 15) received classical bolus [18F]FDG injections and dynamic PET images were recorded. In a second cohort (n = 11) [18F]FDG was constantly infused over the entire functional PET/MRI scans. Resting-state fMRI and [18F]FDG-PET connectivity was evaluated using a graph-theory based correlation approach and compared on whole-brain level and for a default-mode network-like structure. Further, pharmacokinetic and tracer uptake influences on [18F]FDG-PET connectivity results were investigated based on the different PET protocols. By integrating simultaneous resting-state fMRI and dynamic [18F]FDG-PET measurements in the rat brain, we identified homotopic correlations between both modalities, suggesting an underlying synchrony between hemodynamic processes and glucose consumption. Furthermore, the presence of the prominent resting-state default-mode network-like structure was not only depicted on a functional scale but also from dynamic fluctuations of [18F]FDG. In addition, the present findings demonstrated strong pharmacokinetic and tracer uptake dependencies of [18F]FDG-PET connectivity outcomes. This study highlights the application of dynamic [18F]FDG-PET to study cognitive brain functions and to decode underlying brain networks in the resting-state. Thereby, PET-derived connectivity outcomes indicated strong dependencies on tracer application regimens and subsequent time-varying tracer pharmacokinetics.

The Power of Connecting Dots: Advanced Techniques to Evaluate Brain Functional Connectivity in Humans

Brain functional connectivity estimation allows us to depict patterns of cerebral activity not understandable otherwise with the standard brain imaging techniques such as functional magnetic resonance imaging (fMRI) as well as electro or magnetoencephalography (hr-EEG, MEG). This special issue of the IEEE Transactions on Biomedical Engineering reports a range of methodological innovations toward the estimation of functional connectivity from brain activity data, with emphasis on neuroelectric and hemodynamic imaging modalities. Functional connectivity methodologies enable "connecting of the dots" derived from brain activity observations over multiple distributed sites, as depicted by such fMRI and hr-EEG/MEG devices.

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

The Functional Overlap of Executive Control and Language Processing in Bilinguals

The need to control multiple languages is thought to require domain-general executive control (EC) in bilinguals such that the EC and language systems become interdependent. However, there has been no systematic investigation into how and where EC and language processes overlap in the bilingual brain. If the concurrent recruitment of EC during bilingual language processing is domain-general and extends to non-linguistic EC, we hypothesize that regions commonly involvement in language processing, linguistic EC, and non-linguistic EC may be selectively altered in bilinguals compared to monolinguals. A conjunction of functional magnetic resonance imaging (fMRI) data from a flanker task with linguistic and nonlinguistic distractors and a semantic categorization task showed functional overlap in the left inferior frontal gyrus (LIFG) in bilinguals, whereas no overlap occurred in monolinguals. This research therefore identifies a neural locus of functional overlap of language and EC in the bilingual brain.