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Stable modality-specific activity flows as reflected by the neuroenergetic approach to the FMRI weighted maps.

Strelnikov K, Barone P - PLoS ONE (2012)

Bottom Line: The sources, from which activity spreads in the brain during face processing, were detected in the occipital cortex.For auditory word processing, the sources of activity flows were detected bilaterally in the middle superior temporal regions, they were also detected in the left posterior superior temporal cortex.Thus, neuroenergetic assumptions may give a novel perspective for the analysis of neuroimaging data.

View Article: PubMed Central - PubMed

Affiliation: Hopital Purpan, Toulouse, France. kuzma@cerco.ups-tlse.fr

ABSTRACT
This article uses the ideas of neuroenergetic and neural field theories to detect stimulation-driven energy flows in the brain during face and auditory word processing. In this analysis, energy flows are thought to create the stable gradients of the fMRI weighted summary images. The sources, from which activity spreads in the brain during face processing, were detected in the occipital cortex. The following direction of energy flows in the frontal cortex was described: the right inferior frontal = >the left inferior frontal = >the triangular part of the left inferior frontal cortex = >the left operculum. In the left operculum, a localized circuit was described. For auditory word processing, the sources of activity flows were detected bilaterally in the middle superior temporal regions, they were also detected in the left posterior superior temporal cortex. Thus, neuroenergetic assumptions may give a novel perspective for the analysis of neuroimaging data.

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Illustration of divergence and gradients in a single brain slice of one subject during face processing.(A) The levels of divergence in a brain slice as coded by the white-black scale. (B) Each voxel in the slice is presented by an arrow – the direction of the arrow reflects the direction of the fastest change of the signal, the size of the arrow reflects the size of this change. These arrows are gradient vectors in each voxel. (C) The magnified part of B. where gradient vectors diverge. (D) The magnified part of B. where gradient vectors converge.
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pone-0033462-g001: Illustration of divergence and gradients in a single brain slice of one subject during face processing.(A) The levels of divergence in a brain slice as coded by the white-black scale. (B) Each voxel in the slice is presented by an arrow – the direction of the arrow reflects the direction of the fastest change of the signal, the size of the arrow reflects the size of this change. These arrows are gradient vectors in each voxel. (C) The magnified part of B. where gradient vectors diverge. (D) The magnified part of B. where gradient vectors converge.

Mentions: In this present study, we applied vector analysis to the fMRI data (Figure 1). In particular, we used statistical parametric mapping (SPM) to describe gradient vectors and their divergences in the fMRI data at the group level. As the BOLD signal reflects neuroglial activity, these gradients and divergences reflect gradients and divergences of activity in the brain. Compared with the classical activation analysis of fMRI data, which simply indicates where energy turnover is higher, gradients and divergences of the signal help understand the directions of energy flow (i.e., activity propagation) in the brain. The analysis of divergences should not be confused with classical activation analysis, because within the classical activations, which reflect a “plateau” of energy turnover, no divergence may exist; on the contrary, it may exist in the other regions. Positive divergence indicates the regions with the highest flow of energy outside of the region, i.e. sources of energy flows.


Stable modality-specific activity flows as reflected by the neuroenergetic approach to the FMRI weighted maps.

Strelnikov K, Barone P - PLoS ONE (2012)

Illustration of divergence and gradients in a single brain slice of one subject during face processing.(A) The levels of divergence in a brain slice as coded by the white-black scale. (B) Each voxel in the slice is presented by an arrow – the direction of the arrow reflects the direction of the fastest change of the signal, the size of the arrow reflects the size of this change. These arrows are gradient vectors in each voxel. (C) The magnified part of B. where gradient vectors diverge. (D) The magnified part of B. where gradient vectors converge.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3303838&req=5

pone-0033462-g001: Illustration of divergence and gradients in a single brain slice of one subject during face processing.(A) The levels of divergence in a brain slice as coded by the white-black scale. (B) Each voxel in the slice is presented by an arrow – the direction of the arrow reflects the direction of the fastest change of the signal, the size of the arrow reflects the size of this change. These arrows are gradient vectors in each voxel. (C) The magnified part of B. where gradient vectors diverge. (D) The magnified part of B. where gradient vectors converge.
Mentions: In this present study, we applied vector analysis to the fMRI data (Figure 1). In particular, we used statistical parametric mapping (SPM) to describe gradient vectors and their divergences in the fMRI data at the group level. As the BOLD signal reflects neuroglial activity, these gradients and divergences reflect gradients and divergences of activity in the brain. Compared with the classical activation analysis of fMRI data, which simply indicates where energy turnover is higher, gradients and divergences of the signal help understand the directions of energy flow (i.e., activity propagation) in the brain. The analysis of divergences should not be confused with classical activation analysis, because within the classical activations, which reflect a “plateau” of energy turnover, no divergence may exist; on the contrary, it may exist in the other regions. Positive divergence indicates the regions with the highest flow of energy outside of the region, i.e. sources of energy flows.

Bottom Line: The sources, from which activity spreads in the brain during face processing, were detected in the occipital cortex.For auditory word processing, the sources of activity flows were detected bilaterally in the middle superior temporal regions, they were also detected in the left posterior superior temporal cortex.Thus, neuroenergetic assumptions may give a novel perspective for the analysis of neuroimaging data.

View Article: PubMed Central - PubMed

Affiliation: Hopital Purpan, Toulouse, France. kuzma@cerco.ups-tlse.fr

ABSTRACT
This article uses the ideas of neuroenergetic and neural field theories to detect stimulation-driven energy flows in the brain during face and auditory word processing. In this analysis, energy flows are thought to create the stable gradients of the fMRI weighted summary images. The sources, from which activity spreads in the brain during face processing, were detected in the occipital cortex. The following direction of energy flows in the frontal cortex was described: the right inferior frontal = >the left inferior frontal = >the triangular part of the left inferior frontal cortex = >the left operculum. In the left operculum, a localized circuit was described. For auditory word processing, the sources of activity flows were detected bilaterally in the middle superior temporal regions, they were also detected in the left posterior superior temporal cortex. Thus, neuroenergetic assumptions may give a novel perspective for the analysis of neuroimaging data.

Show MeSH
Related in: MedlinePlus