ausblenden:
Schlagwörter:
-
Zusammenfassung:
Introduction:
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. While there exist many algorithms for resting state data, the same cannot be said for task-based fMRI. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Our new method identifies task-related changes in network configuration without requiring presegmentations so that the spatial resolution of the input data is preserved. Furthermore, it is free from any specific hemodynamic response model so that it is capable of detecting many different types of responses to task changes.
Methods:
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 is designed to identify time series of voxels in an fMRI image that collectively synchronize in response to a task. At the heart of our approach is the concept of spatially localized and task-induced edge density motivating us to call this algorithm "TED" (Task-induced Edge Density). In short, TED identifies edges in a brain network that differentially respond in unison to a task onset and that occur in dense packs of edges with similar characteristics. Figure 1 illustrates this concept: the TED measure counts the percentage of supra-threshold edges connecting local neighbourhoods. A detailed description of the algorithm can be found in Lohmann et al. (2015). We applied TED to task-based fMRI data provided by the Human Connectome Project focusing on the social recognition task, see Barch et al (2013). Minimally preprocessed data of 100 subjects were included. We contrasted two conditions called 'mental' and 'random'. Statistical inference was based on false discovery rates (FDR) using 1000 permutations to derive a null distribution.
Results:
TED identified several task-specific, large-scale patterns of task-related synchronization. Figure 2 shows the result as a hubness map, FDR corrected at p < 0.05. A voxel in the hubness map records the number of edges for which this voxel serves as an endpoint. Voxels in which many edges accumulate may be viewed as hubs in a task-specific network, and the number of edges meeting in a voxel is a measure of the voxel's hubness. Note that the left and right temporal poles appear as extremely strong hubs indicating that they may serve as integration areas, see e.g. Pascual et al. (2015). Figure 3 shows an alternative visualization. Here edges passing through a pre-defined ROI are shown.
Conclusions:
We found TED to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical approaches such as the false discovery rate. A major advantage of TED compared to other network-based methods is that it does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. Because its conceptual basis is task-induced synchronization it does not depend on a hemodynamic response model. We conclude that the new TED method provides us with an entirely new window into the immense complexity of human brain function.
