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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.


Sketch for regions of interest (ROIs). Fifteen different ROIs were extracted from three different resting state networks: 1 ROI in the sensory motor (SM), 6 ROIs in the default mode network (DMN), and 8 ROIs in the executive control (ExC).
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Figure 1: Sketch for regions of interest (ROIs). Fifteen different ROIs were extracted from three different resting state networks: 1 ROI in the sensory motor (SM), 6 ROIs in the default mode network (DMN), and 8 ROIs in the executive control (ExC).

Mentions: Specifically, the following three RSNs were selected: the default mode network (DMN), the executive control (ExC) network and the sensory motor (SM) network. Next, these three networks were manually subdivided in distinct spatially contiguous regions (see Figure 1). For each region, a region growing segmentation method was applied by manually selecting a seed region, thus obtaining a total of 15 different ROIs: 1 SM region, 6 DMNs and 8 ExCs regions. In particular, the “island effect” method incorporated in 3D Slicer (http://www.slicer.org) was applied, which selects all the voxels of the contiguous region given an initial seed. Visual representations of all ROIs are given in Figure 1 and their sizes in Table 1.


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)

Sketch for regions of interest (ROIs). Fifteen different ROIs were extracted from three different resting state networks: 1 ROI in the sensory motor (SM), 6 ROIs in the default mode network (DMN), and 8 ROIs in the executive control (ExC).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Sketch for regions of interest (ROIs). Fifteen different ROIs were extracted from three different resting state networks: 1 ROI in the sensory motor (SM), 6 ROIs in the default mode network (DMN), and 8 ROIs in the executive control (ExC).
Mentions: Specifically, the following three RSNs were selected: the default mode network (DMN), the executive control (ExC) network and the sensory motor (SM) network. Next, these three networks were manually subdivided in distinct spatially contiguous regions (see Figure 1). For each region, a region growing segmentation method was applied by manually selecting a seed region, thus obtaining a total of 15 different ROIs: 1 SM region, 6 DMNs and 8 ExCs regions. In particular, the “island effect” method incorporated in 3D Slicer (http://www.slicer.org) was applied, which selects all the voxels of the contiguous region given an initial seed. Visual representations of all ROIs are given in Figure 1 and their sizes in Table 1.

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.