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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

(A) Mean physical distance between seeds and their kth neighbors, where neighbors are ranked according to three metrics: Euclidean (red), Connectome (blue), and Randomized (gray) (averaged across all ROIs, with solid line representing mean physical distance and shaded areas indicating first and third quartiles). (B) Mean Pairwise MI between pairs of ROIs separated by different Euclidean (horizontal axis) and Connectome (vertical axis) distances. Log color scaling used to highlight differences among weakly coupled connections. Upper bar chart shows mean Pairwise MI values for pairs of ROIs separated by different Euclidean distances (irrespective of Connectome distances) while bar chart on right shows mean Pairwise MI values for pairs of ROIs separated by different Connectome distances (irrespective of Euclidean distances).
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Figure 2: (A) Mean physical distance between seeds and their kth neighbors, where neighbors are ranked according to three metrics: Euclidean (red), Connectome (blue), and Randomized (gray) (averaged across all ROIs, with solid line representing mean physical distance and shaded areas indicating first and third quartiles). (B) Mean Pairwise MI between pairs of ROIs separated by different Euclidean (horizontal axis) and Connectome (vertical axis) distances. Log color scaling used to highlight differences among weakly coupled connections. Upper bar chart shows mean Pairwise MI values for pairs of ROIs separated by different Euclidean distances (irrespective of Connectome distances) while bar chart on right shows mean Pairwise MI values for pairs of ROIs separated by different Connectome distances (irrespective of Euclidean distances).

Mentions: We first characterized the distance in physical space between seeds and neighbors ranked according to different metrics (Euclidean, Connectome and Randomized). In Figure 2A, the Y-axis depicts the Euclidean distance (mm) between seed ROIs and the kth-neighbor (X-axis) chosen according to the three metrics, averaged across all seed ROIs in both hemispheres (shaded areas reflect 1st and 3rd quartiles). The physical distance to nearby Connectome neighbors tends to be small, though highly variable across seeds and not as small as to Euclidean neighbors, which are by definition maximally proximate in physical space. Randomized neighbors display no spatial regularity, with average distance to neighbor of any rank corresponding to the expected Euclidean distance separating randomly chosen pairs of ROIs (~65 mm).


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) Mean physical distance between seeds and their kth neighbors, where neighbors are ranked according to three metrics: Euclidean (red), Connectome (blue), and Randomized (gray) (averaged across all ROIs, with solid line representing mean physical distance and shaded areas indicating first and third quartiles). (B) Mean Pairwise MI between pairs of ROIs separated by different Euclidean (horizontal axis) and Connectome (vertical axis) distances. Log color scaling used to highlight differences among weakly coupled connections. Upper bar chart shows mean Pairwise MI values for pairs of ROIs separated by different Euclidean distances (irrespective of Connectome distances) while bar chart on right shows mean Pairwise MI values for pairs of ROIs separated by different Connectome distances (irrespective of Euclidean distances).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: (A) Mean physical distance between seeds and their kth neighbors, where neighbors are ranked according to three metrics: Euclidean (red), Connectome (blue), and Randomized (gray) (averaged across all ROIs, with solid line representing mean physical distance and shaded areas indicating first and third quartiles). (B) Mean Pairwise MI between pairs of ROIs separated by different Euclidean (horizontal axis) and Connectome (vertical axis) distances. Log color scaling used to highlight differences among weakly coupled connections. Upper bar chart shows mean Pairwise MI values for pairs of ROIs separated by different Euclidean distances (irrespective of Connectome distances) while bar chart on right shows mean Pairwise MI values for pairs of ROIs separated by different Connectome distances (irrespective of Euclidean distances).
Mentions: We first characterized the distance in physical space between seeds and neighbors ranked according to different metrics (Euclidean, Connectome and Randomized). In Figure 2A, the Y-axis depicts the Euclidean distance (mm) between seed ROIs and the kth-neighbor (X-axis) chosen according to the three metrics, averaged across all seed ROIs in both hemispheres (shaded areas reflect 1st and 3rd quartiles). The physical distance to nearby Connectome neighbors tends to be small, though highly variable across seeds and not as small as to Euclidean neighbors, which are by definition maximally proximate in physical space. Randomized neighbors display no spatial regularity, with average distance to neighbor of any rank corresponding to the expected Euclidean distance separating randomly chosen pairs of ROIs (~65 mm).

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