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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) Pearson Correlation coefficient values between Seed-Environment MIs and the long-range efficiency to environmental ROIs as increasingly distant Euclidean environments are considered. Efficiency values are computed using distances defined on the three metrics. The vertical dotted line indicates subsystem size 124, where the maximal correlation value of ~0.47 is observed, between Seed-Environment MI and Connectome efficiency. (B) Map of the cortical distribution of Seed-Environment MI for environments of Euclidean subsystems of size 124. (C) Map of the cortical distribution of Connectome efficiency values between seeds and environments of Euclidean subsystems of size 124.
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Figure 5: (A) Pearson Correlation coefficient values between Seed-Environment MIs and the long-range efficiency to environmental ROIs as increasingly distant Euclidean environments are considered. Efficiency values are computed using distances defined on the three metrics. The vertical dotted line indicates subsystem size 124, where the maximal correlation value of ~0.47 is observed, between Seed-Environment MI and Connectome efficiency. (B) Map of the cortical distribution of Seed-Environment MI for environments of Euclidean subsystems of size 124. (C) Map of the cortical distribution of Connectome efficiency values between seeds and environments of Euclidean subsystems of size 124.

Mentions: Figure 5A shows the Pearson correlation values between the Seed-Environment MI and the three long-range efficiency measures as increasingly long Euclidean distances are considered (with increasing subsystem size on the X-axis, environments become increasingly small and distant). Correlations are computed separately across all seed ROIs within each hemisphere and then averaged between hemispheres. Correlations are highest between Seed-Environment MI and long-range efficiency values over the Connectome metric. They reach a peak correlation value of ~0.47 at k = 124 (vertical dotted line), corresponding to environments composed of ROIs located further than ~65 mm from the seed. Such a strong correlation was not observed for efficiency values computed using either of the other two metrics at any scale.


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) Pearson Correlation coefficient values between Seed-Environment MIs and the long-range efficiency to environmental ROIs as increasingly distant Euclidean environments are considered. Efficiency values are computed using distances defined on the three metrics. The vertical dotted line indicates subsystem size 124, where the maximal correlation value of ~0.47 is observed, between Seed-Environment MI and Connectome efficiency. (B) Map of the cortical distribution of Seed-Environment MI for environments of Euclidean subsystems of size 124. (C) Map of the cortical distribution of Connectome efficiency values between seeds and environments of Euclidean subsystems of size 124.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: (A) Pearson Correlation coefficient values between Seed-Environment MIs and the long-range efficiency to environmental ROIs as increasingly distant Euclidean environments are considered. Efficiency values are computed using distances defined on the three metrics. The vertical dotted line indicates subsystem size 124, where the maximal correlation value of ~0.47 is observed, between Seed-Environment MI and Connectome efficiency. (B) Map of the cortical distribution of Seed-Environment MI for environments of Euclidean subsystems of size 124. (C) Map of the cortical distribution of Connectome efficiency values between seeds and environments of Euclidean subsystems of size 124.
Mentions: Figure 5A shows the Pearson correlation values between the Seed-Environment MI and the three long-range efficiency measures as increasingly long Euclidean distances are considered (with increasing subsystem size on the X-axis, environments become increasingly small and distant). Correlations are computed separately across all seed ROIs within each hemisphere and then averaged between hemispheres. Correlations are highest between Seed-Environment MI and long-range efficiency values over the Connectome metric. They reach a peak correlation value of ~0.47 at k = 124 (vertical dotted line), corresponding to environments composed of ROIs located further than ~65 mm from the seed. Such a strong correlation was not observed for efficiency values computed using either of the other two metrics at any scale.

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.