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Lagged and instantaneous dynamical influences related to brain structural connectivity.

Alonso-Montes C, Diez I, Remaki L, Escudero I, Mateos B, Rosseel Y, Marinazzo D, Stramaglia S, Cortes JM - Front Psychol (2015)

Bottom Line: We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC).Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions.We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.

View Article: PubMed Central - PubMed

Affiliation: Basque Center for Applied Mathematics Bilbao, Spain.

ABSTRACT
Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2(*) signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.

No MeSH data available.


Structural, effective and functional connectivity matrices (SC, EC and FC, respectively). (A1) SC matrix calculated by the fiber number. Because many of the values in this matrix are very small, we plotted it in logarithmic scale only to enhance visibility. (A2–A4) EC (eSEM) and FC matrices (C and PC), all of them normalized in the [0, 1] range for comparison purposes. (B) Correlation-based similarity between SC and eSEM, C and PC, calculated either over all pairs or only on connected pairs. (C) Mean values of connectivity matrices separated in two groups: pairs such that they have non-zero fibers between them (structurally connected pairs, CP) and non-connected pairs (NCP). *p<0.01, otherwise means no statistical significance.
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Figure 2: Structural, effective and functional connectivity matrices (SC, EC and FC, respectively). (A1) SC matrix calculated by the fiber number. Because many of the values in this matrix are very small, we plotted it in logarithmic scale only to enhance visibility. (A2–A4) EC (eSEM) and FC matrices (C and PC), all of them normalized in the [0, 1] range for comparison purposes. (B) Correlation-based similarity between SC and eSEM, C and PC, calculated either over all pairs or only on connected pairs. (C) Mean values of connectivity matrices separated in two groups: pairs such that they have non-zero fibers between them (structurally connected pairs, CP) and non-connected pairs (NCP). *p<0.01, otherwise means no statistical significance.

Mentions: Next, the average across subjects SC matrix (Figure 2A1) was computed by averaging the fiber number between pairs of ROIs. Notice that SC is a matrix with many near-zero values. So, it is represented in logarithmic scale just to improve visualization, but all the analyses were performed using the original SC matrix.


Lagged and instantaneous dynamical influences related to brain structural connectivity.

Alonso-Montes C, Diez I, Remaki L, Escudero I, Mateos B, Rosseel Y, Marinazzo D, Stramaglia S, Cortes JM - Front Psychol (2015)

Structural, effective and functional connectivity matrices (SC, EC and FC, respectively). (A1) SC matrix calculated by the fiber number. Because many of the values in this matrix are very small, we plotted it in logarithmic scale only to enhance visibility. (A2–A4) EC (eSEM) and FC matrices (C and PC), all of them normalized in the [0, 1] range for comparison purposes. (B) Correlation-based similarity between SC and eSEM, C and PC, calculated either over all pairs or only on connected pairs. (C) Mean values of connectivity matrices separated in two groups: pairs such that they have non-zero fibers between them (structurally connected pairs, CP) and non-connected pairs (NCP). *p<0.01, otherwise means no statistical significance.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508482&req=5

Figure 2: Structural, effective and functional connectivity matrices (SC, EC and FC, respectively). (A1) SC matrix calculated by the fiber number. Because many of the values in this matrix are very small, we plotted it in logarithmic scale only to enhance visibility. (A2–A4) EC (eSEM) and FC matrices (C and PC), all of them normalized in the [0, 1] range for comparison purposes. (B) Correlation-based similarity between SC and eSEM, C and PC, calculated either over all pairs or only on connected pairs. (C) Mean values of connectivity matrices separated in two groups: pairs such that they have non-zero fibers between them (structurally connected pairs, CP) and non-connected pairs (NCP). *p<0.01, otherwise means no statistical significance.
Mentions: Next, the average across subjects SC matrix (Figure 2A1) was computed by averaging the fiber number between pairs of ROIs. Notice that SC is a matrix with many near-zero values. So, it is represented in logarithmic scale just to improve visualization, but all the analyses were performed using the original SC matrix.

Bottom Line: We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC).Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions.We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.

View Article: PubMed Central - PubMed

Affiliation: Basque Center for Applied Mathematics Bilbao, Spain.

ABSTRACT
Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2(*) signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.

No MeSH data available.