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Multi-scale integration and predictability in resting state brain activity.

Kolchinsky A, van den Heuvel MP, Griffa A, Hagmann P, Rocha LM, Sporns O, Goñi J - Front Neuroinform (2014)

Bottom Line: We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered.We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks.Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics, School of Informatics and Computing, Indiana University Bloomington, IN, USA ; Instituto Gulbenkian de Ciência Oeiras, Portugal.

ABSTRACT
The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.

No MeSH data available.


Related in: MedlinePlus

Data processing pipeline. (A) The steps used to create the final dataset of pooled subject ROI time series, pooled subject ROIs coordinates, and pooled connectome [using the number of fibers (NOF) measure]. (B)Euclidean Distance is computed as the physical distance between the centroids of ROI coordinates. At bottom are shown two example Euclidean sets of size 20 ROIs centered on two “seed” ROIs: one seed in the precuneus region (seed in dark red and the rest of the subsystem in light red) and one seed in the frontal pole region (seed in dark blue and the rest of the subsystem in light blue). (C) NOF structural connectivity matrix is transformed into a Connectome Dissimilarity matrix. Shortest paths on the dissimilarity graph are used to create the Connectome distance metric. Two examples of Connectome sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (D) A degree preserving rewiring is used to create a randomized structural connectivity matrix. This is transformed into a Randomized dissimilarity matrix. Shortest path distances on the corresponding dissimilarity graph are used to create the Randomized distance metric. Two example Randomized sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (E) The subsystems and time series are used to calculate a set of information-theoretic measures of predictability and integration.
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Figure 1: Data processing pipeline. (A) The steps used to create the final dataset of pooled subject ROI time series, pooled subject ROIs coordinates, and pooled connectome [using the number of fibers (NOF) measure]. (B)Euclidean Distance is computed as the physical distance between the centroids of ROI coordinates. At bottom are shown two example Euclidean sets of size 20 ROIs centered on two “seed” ROIs: one seed in the precuneus region (seed in dark red and the rest of the subsystem in light red) and one seed in the frontal pole region (seed in dark blue and the rest of the subsystem in light blue). (C) NOF structural connectivity matrix is transformed into a Connectome Dissimilarity matrix. Shortest paths on the dissimilarity graph are used to create the Connectome distance metric. Two examples of Connectome sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (D) A degree preserving rewiring is used to create a randomized structural connectivity matrix. This is transformed into a Randomized dissimilarity matrix. Shortest path distances on the corresponding dissimilarity graph are used to create the Randomized distance metric. Two example Randomized sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (E) The subsystems and time series are used to calculate a set of information-theoretic measures of predictability and integration.

Mentions: The processing steps used to create the final dataset of pooled subject ROI time series, pooled subject ROIs coordinates, and pooled subject structural connectivity are diagrammed in Figure 1A.


Multi-scale integration and predictability in resting state brain activity.

Kolchinsky A, van den Heuvel MP, Griffa A, Hagmann P, Rocha LM, Sporns O, Goñi J - Front Neuroinform (2014)

Data processing pipeline. (A) The steps used to create the final dataset of pooled subject ROI time series, pooled subject ROIs coordinates, and pooled connectome [using the number of fibers (NOF) measure]. (B)Euclidean Distance is computed as the physical distance between the centroids of ROI coordinates. At bottom are shown two example Euclidean sets of size 20 ROIs centered on two “seed” ROIs: one seed in the precuneus region (seed in dark red and the rest of the subsystem in light red) and one seed in the frontal pole region (seed in dark blue and the rest of the subsystem in light blue). (C) NOF structural connectivity matrix is transformed into a Connectome Dissimilarity matrix. Shortest paths on the dissimilarity graph are used to create the Connectome distance metric. Two examples of Connectome sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (D) A degree preserving rewiring is used to create a randomized structural connectivity matrix. This is transformed into a Randomized dissimilarity matrix. Shortest path distances on the corresponding dissimilarity graph are used to create the Randomized distance metric. Two example Randomized sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (E) The subsystems and time series are used to calculate a set of information-theoretic measures of predictability and integration.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109611&req=5

Figure 1: Data processing pipeline. (A) The steps used to create the final dataset of pooled subject ROI time series, pooled subject ROIs coordinates, and pooled connectome [using the number of fibers (NOF) measure]. (B)Euclidean Distance is computed as the physical distance between the centroids of ROI coordinates. At bottom are shown two example Euclidean sets of size 20 ROIs centered on two “seed” ROIs: one seed in the precuneus region (seed in dark red and the rest of the subsystem in light red) and one seed in the frontal pole region (seed in dark blue and the rest of the subsystem in light blue). (C) NOF structural connectivity matrix is transformed into a Connectome Dissimilarity matrix. Shortest paths on the dissimilarity graph are used to create the Connectome distance metric. Two examples of Connectome sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (D) A degree preserving rewiring is used to create a randomized structural connectivity matrix. This is transformed into a Randomized dissimilarity matrix. Shortest path distances on the corresponding dissimilarity graph are used to create the Randomized distance metric. Two example Randomized sets of 20 ROIs are shown, centered on the same two ROIs as in (B). (E) The subsystems and time series are used to calculate a set of information-theoretic measures of predictability and integration.
Mentions: The processing steps used to create the final dataset of pooled subject ROI time series, pooled subject ROIs coordinates, and pooled subject structural connectivity are diagrammed in Figure 1A.

Bottom Line: We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered.We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks.Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics, School of Informatics and Computing, Indiana University Bloomington, IN, USA ; Instituto Gulbenkian de Ciência Oeiras, Portugal.

ABSTRACT
The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.

No MeSH data available.


Related in: MedlinePlus