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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.


Scaling of the subsystem predictability measures. Colored lines indicate mean values across all subsystems, while shaded areas indicate values within 1st and 3rd quartile. Red, blue, and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. (A) Subsystem integration per ROI, showing total correlation in the joint activity of ROIs in subsystems of different sizes. The illustration in the lower right corner diagrams how this measure is computed for a given subsystem of size 3. (B) Subsystem-Environment MI, showing functional coupling between subsystems and environments for different sizes. The illustration in the top right corner diagrams how this measure is computed for a given subsystem of size 3 and its environment.
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Figure 6: Scaling of the subsystem predictability measures. Colored lines indicate mean values across all subsystems, while shaded areas indicate values within 1st and 3rd quartile. Red, blue, and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. (A) Subsystem integration per ROI, showing total correlation in the joint activity of ROIs in subsystems of different sizes. The illustration in the lower right corner diagrams how this measure is computed for a given subsystem of size 3. (B) Subsystem-Environment MI, showing functional coupling between subsystems and environments for different sizes. The illustration in the top right corner diagrams how this measure is computed for a given subsystem of size 3 and its environment.

Mentions: Figure 6A shows the Subsystem Integration per ROI, which quantifies the amount of total correlation of subsystem activity (divided by subsystem size for normalization purposes). The diagram in the lower right of the figure shows in schematic form how this measure is computed (brown is the subsystem, and the three-pointed green arrow is total correlation). On average, the most integrated subsystems up to size ~90 ROIs are those defined according to the Euclidean metric (size-90 Euclidean subsystems have a radius of ~55 mm), while subsystems defined according to the Connectome are on average the most integrated for larger subsystem sizes. As expected, subsystems selected according to the Randomized metric, which are neither spatially co-located nor densely structurally interconnected, display a much lower level of multivariate 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)

Scaling of the subsystem predictability measures. Colored lines indicate mean values across all subsystems, while shaded areas indicate values within 1st and 3rd quartile. Red, blue, and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. (A) Subsystem integration per ROI, showing total correlation in the joint activity of ROIs in subsystems of different sizes. The illustration in the lower right corner diagrams how this measure is computed for a given subsystem of size 3. (B) Subsystem-Environment MI, showing functional coupling between subsystems and environments for different sizes. The illustration in the top right corner diagrams how this measure is computed for a given subsystem of size 3 and its environment.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Scaling of the subsystem predictability measures. Colored lines indicate mean values across all subsystems, while shaded areas indicate values within 1st and 3rd quartile. Red, blue, and gray colors correspond to subsystems chosen according to Euclidean, Connectome and Randomized metrics respectively. (A) Subsystem integration per ROI, showing total correlation in the joint activity of ROIs in subsystems of different sizes. The illustration in the lower right corner diagrams how this measure is computed for a given subsystem of size 3. (B) Subsystem-Environment MI, showing functional coupling between subsystems and environments for different sizes. The illustration in the top right corner diagrams how this measure is computed for a given subsystem of size 3 and its environment.
Mentions: Figure 6A shows the Subsystem Integration per ROI, which quantifies the amount of total correlation of subsystem activity (divided by subsystem size for normalization purposes). The diagram in the lower right of the figure shows in schematic form how this measure is computed (brown is the subsystem, and the three-pointed green arrow is total correlation). On average, the most integrated subsystems up to size ~90 ROIs are those defined according to the Euclidean metric (size-90 Euclidean subsystems have a radius of ~55 mm), while subsystems defined according to the Connectome are on average the most integrated for larger subsystem sizes. As expected, subsystems selected according to the Randomized metric, which are neither spatially co-located nor densely structurally interconnected, display a much lower level of multivariate 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.