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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.


(A) Scatter plot of Subsystem Integration vs. Subsystem-Environment MI for subsystems of size 11, with red, blue and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. Left-hemisphere subsystems are indicated with left-pointing triangles and right-hemisphere subsystems are indicated with right-pointing triangles. (B) Connectome subsystems in the upper 50 percentile of both Subsystem Integration and Subsystem-Environment MI were chosen and allowed us to identify four minimally overlapping “subsystem communities” in the left and right hemispheres. ROIs are colored according to community membership (color arbitrary); gray ROIs are those that did not belong to any high-Subsystem-Integration, high-Subsystem-Environment MI subsystem. (C) The distribution of subsystem communities across anatomical areas. Bar chart shows the number of ROIs from each community that are contained in different anatomical areas for the top 9 represented anatomical areas. Bar chart colors correspond to the colors used on the cortical map.
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Figure 7: (A) Scatter plot of Subsystem Integration vs. Subsystem-Environment MI for subsystems of size 11, with red, blue and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. Left-hemisphere subsystems are indicated with left-pointing triangles and right-hemisphere subsystems are indicated with right-pointing triangles. (B) Connectome subsystems in the upper 50 percentile of both Subsystem Integration and Subsystem-Environment MI were chosen and allowed us to identify four minimally overlapping “subsystem communities” in the left and right hemispheres. ROIs are colored according to community membership (color arbitrary); gray ROIs are those that did not belong to any high-Subsystem-Integration, high-Subsystem-Environment MI subsystem. (C) The distribution of subsystem communities across anatomical areas. Bar chart shows the number of ROIs from each community that are contained in different anatomical areas for the top 9 represented anatomical areas. Bar chart colors correspond to the colors used on the cortical map.

Mentions: Overall, Figure 6 shows that Connectome subsystems exhibit both high Subsystem-Environment MI and high Subsystem Integration. We explored this finding in more depth in the following figure. First, we selected all Connectome subsystems of size of 11, corresponding to a volume of approximately 5% of each hemisphere (as seen in Figure 6A, at this size Connectome subsystems are on average nearly as integrated as Euclidean subsystems but, as Figure 6B shows, contain much information about their environments). Figure 7A shows the scatter plot of Subsystem Integration (X-Axis) vs. Subsystem-Environment MI (Y-Axis) for size-11 subsystems defined according to Euclidean, Connectome and Randomized metrics. Randomized Subsystems (gray) tend to cluster in regions of the scatter plot characterized by high Subsystem-Environment MI (lack of segregation from environment) and low Subsystem Integration (lack of internal integration). Euclidean Subsystems (red) tend to occupy regions of the scatter plot characterized by low Subsystem-Environment MI (high segregation from environment) and high Subsystem Integration (high internal integration). Connectome Subsystems (blue), however, occupy intermediate regions of the scatter plot, demonstrating significant amounts of both Subsystem-Environment MI (thus not being functionally segregated from the rest of the hemisphere) while also having significant Subsystem Integration (thus also having internal integration).


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)

(A) Scatter plot of Subsystem Integration vs. Subsystem-Environment MI for subsystems of size 11, with red, blue and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. Left-hemisphere subsystems are indicated with left-pointing triangles and right-hemisphere subsystems are indicated with right-pointing triangles. (B) Connectome subsystems in the upper 50 percentile of both Subsystem Integration and Subsystem-Environment MI were chosen and allowed us to identify four minimally overlapping “subsystem communities” in the left and right hemispheres. ROIs are colored according to community membership (color arbitrary); gray ROIs are those that did not belong to any high-Subsystem-Integration, high-Subsystem-Environment MI subsystem. (C) The distribution of subsystem communities across anatomical areas. Bar chart shows the number of ROIs from each community that are contained in different anatomical areas for the top 9 represented anatomical areas. Bar chart colors correspond to the colors used on the cortical map.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: (A) Scatter plot of Subsystem Integration vs. Subsystem-Environment MI for subsystems of size 11, with red, blue and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. Left-hemisphere subsystems are indicated with left-pointing triangles and right-hemisphere subsystems are indicated with right-pointing triangles. (B) Connectome subsystems in the upper 50 percentile of both Subsystem Integration and Subsystem-Environment MI were chosen and allowed us to identify four minimally overlapping “subsystem communities” in the left and right hemispheres. ROIs are colored according to community membership (color arbitrary); gray ROIs are those that did not belong to any high-Subsystem-Integration, high-Subsystem-Environment MI subsystem. (C) The distribution of subsystem communities across anatomical areas. Bar chart shows the number of ROIs from each community that are contained in different anatomical areas for the top 9 represented anatomical areas. Bar chart colors correspond to the colors used on the cortical map.
Mentions: Overall, Figure 6 shows that Connectome subsystems exhibit both high Subsystem-Environment MI and high Subsystem Integration. We explored this finding in more depth in the following figure. First, we selected all Connectome subsystems of size of 11, corresponding to a volume of approximately 5% of each hemisphere (as seen in Figure 6A, at this size Connectome subsystems are on average nearly as integrated as Euclidean subsystems but, as Figure 6B shows, contain much information about their environments). Figure 7A shows the scatter plot of Subsystem Integration (X-Axis) vs. Subsystem-Environment MI (Y-Axis) for size-11 subsystems defined according to Euclidean, Connectome and Randomized metrics. Randomized Subsystems (gray) tend to cluster in regions of the scatter plot characterized by high Subsystem-Environment MI (lack of segregation from environment) and low Subsystem Integration (lack of internal integration). Euclidean Subsystems (red) tend to occupy regions of the scatter plot characterized by low Subsystem-Environment MI (high segregation from environment) and high Subsystem Integration (high internal integration). Connectome Subsystems (blue), however, occupy intermediate regions of the scatter plot, demonstrating significant amounts of both Subsystem-Environment MI (thus not being functionally segregated from the rest of the hemisphere) while also having significant Subsystem Integration (thus also having internal integration).

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.