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

Lohmann G, Stelzer J, Zuber V, Buschmann T, Margulies D, Bartels A, Scheffler K - PLoS ONE (2016)

Bottom Line: 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 show that our proposed method provides an entirely new window into the immense complexity of human brain function.

View Article: PubMed Central - PubMed

Affiliation: University Hospital, Department of Biomedical Magnetic Resonance Imaging, University of Tübingen, Tübingen, Germany.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Task-dependent dynamic reconfiguration of whole-brain networks.We depict the reconfiguration using hubness maps on basis of fMRI data fingertapping data of the Human Connectome Project. The hubness maps indicate the number of network edges that feature a significant change between the two experimental conditions. The top row (A) contrasts right hand minus left hand tapping, the bottom row (B) shows the reverse contrast. The colours encode the number of edges with Fdr < 0.05 having one of their endpoints in the respective colour-coded voxel and ranges from 1 to 1000. This number can be interpreted as a measure of “hubness”. Thus, red values in the above figure indicate hubs where many edges accumulate in a voxel. See also S1 and S2 Figs.
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pone.0158185.g005: Task-dependent dynamic reconfiguration of whole-brain networks.We depict the reconfiguration using hubness maps on basis of fMRI data fingertapping data of the Human Connectome Project. The hubness maps indicate the number of network edges that feature a significant change between the two experimental conditions. The top row (A) contrasts right hand minus left hand tapping, the bottom row (B) shows the reverse contrast. The colours encode the number of edges with Fdr < 0.05 having one of their endpoints in the respective colour-coded voxel and ranges from 1 to 1000. This number can be interpreted as a measure of “hubness”. Thus, red values in the above figure indicate hubs where many edges accumulate in a voxel. See also S1 and S2 Figs.

Mentions: Fig 5 shows a resulting hubness map produced as described above in step 6. Voxels that are colour-coded are endpoints in an edge significantly affected by the task. 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”. The upper panel indicates the hubness for edges in the contrast right hand minus left hand. Tapping with the right hand as opposed to the left increased the global connectivity in supplementary motor areas, right and left motor cortex, somatosensory areas, the frontal eye fields, regions in the parietal cortex and the visual cortex. On the other hand, as shown in the lower panel, left hand tapping minus right hand tapping seemed to increase the global connectivity within the bilateral motor network, the default mode network, bilateral putamen, bilateral V5, insular cortex and regions in the cerebellum.


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

Lohmann G, Stelzer J, Zuber V, Buschmann T, Margulies D, Bartels A, Scheffler K - PLoS ONE (2016)

Task-dependent dynamic reconfiguration of whole-brain networks.We depict the reconfiguration using hubness maps on basis of fMRI data fingertapping data of the Human Connectome Project. The hubness maps indicate the number of network edges that feature a significant change between the two experimental conditions. The top row (A) contrasts right hand minus left hand tapping, the bottom row (B) shows the reverse contrast. The colours encode the number of edges with Fdr < 0.05 having one of their endpoints in the respective colour-coded voxel and ranges from 1 to 1000. This number can be interpreted as a measure of “hubness”. Thus, red values in the above figure indicate hubs where many edges accumulate in a voxel. See also S1 and S2 Figs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920409&req=5

pone.0158185.g005: Task-dependent dynamic reconfiguration of whole-brain networks.We depict the reconfiguration using hubness maps on basis of fMRI data fingertapping data of the Human Connectome Project. The hubness maps indicate the number of network edges that feature a significant change between the two experimental conditions. The top row (A) contrasts right hand minus left hand tapping, the bottom row (B) shows the reverse contrast. The colours encode the number of edges with Fdr < 0.05 having one of their endpoints in the respective colour-coded voxel and ranges from 1 to 1000. This number can be interpreted as a measure of “hubness”. Thus, red values in the above figure indicate hubs where many edges accumulate in a voxel. See also S1 and S2 Figs.
Mentions: Fig 5 shows a resulting hubness map produced as described above in step 6. Voxels that are colour-coded are endpoints in an edge significantly affected by the task. 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”. The upper panel indicates the hubness for edges in the contrast right hand minus left hand. Tapping with the right hand as opposed to the left increased the global connectivity in supplementary motor areas, right and left motor cortex, somatosensory areas, the frontal eye fields, regions in the parietal cortex and the visual cortex. On the other hand, as shown in the lower panel, left hand tapping minus right hand tapping seemed to increase the global connectivity within the bilateral motor network, the default mode network, bilateral putamen, bilateral V5, insular cortex and regions in the cerebellum.

Bottom Line: 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 show that our proposed method provides an entirely new window into the immense complexity of human brain function.

View Article: PubMed Central - PubMed

Affiliation: University Hospital, Department of Biomedical Magnetic Resonance Imaging, University of Tübingen, Tübingen, Germany.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus