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Beyond the "Pain Matrix," inter-run synchronization during mechanical nociceptive stimulation.

Cauda F, Costa T, Diano M, Duca S, Torta DM - Front Hum Neurosci (2014)

Bottom Line: One hypothesis is that the traditional analysis of pain-related cerebral responses, by relying on the correlation of a predictor and the canonical hemodynamic response function (HRF)- the general linear model (GLM)- may under-detect the activity of those areas involved in stimulus processing that do not present a canonical HRF.With this method we were able to evidence the involvement of several brain regions that are not usually found when using predictor-based analysis.These areas are synchronized during the administration of mechanical punctate stimuli and are characterized by a BOLD response different from the canonical HRF.

View Article: PubMed Central - PubMed

Affiliation: GCS fMRI, Koelliker Hospital and Department of Psychology, University of Turin Turin, Italy ; Department of Psychology, University of Turin Turin, Italy.

ABSTRACT
Pain is a complex experience that is thought to emerge from the activity of multiple brain areas, some of which are inconsistently detected using traditional fMRI analysis. One hypothesis is that the traditional analysis of pain-related cerebral responses, by relying on the correlation of a predictor and the canonical hemodynamic response function (HRF)- the general linear model (GLM)- may under-detect the activity of those areas involved in stimulus processing that do not present a canonical HRF. In this study, we employed an innovative data-driven processing approach- an inter-run synchronization (IRS) analysis- that has the advantage of not establishing any pre-determined predictor definition. With this method we were able to evidence the involvement of several brain regions that are not usually found when using predictor-based analysis. These areas are synchronized during the administration of mechanical punctate stimuli and are characterized by a BOLD response different from the canonical HRF. This finding opens to new approaches in the study of pain imaging.

No MeSH data available.


Related in: MedlinePlus

Nuisance regression. This map shows a comparison between the results obtained before and after removing motion, white matter and cerebrospinal fluid covariates from the dataset.
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Figure 4: Nuisance regression. This map shows a comparison between the results obtained before and after removing motion, white matter and cerebrospinal fluid covariates from the dataset.

Mentions: In order to exclude the effect of physiological artifacts on our data, we also performed the same analyses using motion, white matter (WM) and cerebrospinal fluid (CSF) as covariates. We show the difference between the two analyses in Figure 4. As it can be seen, differences were minimal, therefore confirming that the nuisances were not biasing significantly our results.


Beyond the "Pain Matrix," inter-run synchronization during mechanical nociceptive stimulation.

Cauda F, Costa T, Diano M, Duca S, Torta DM - Front Hum Neurosci (2014)

Nuisance regression. This map shows a comparison between the results obtained before and after removing motion, white matter and cerebrospinal fluid covariates from the dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4017139&req=5

Figure 4: Nuisance regression. This map shows a comparison between the results obtained before and after removing motion, white matter and cerebrospinal fluid covariates from the dataset.
Mentions: In order to exclude the effect of physiological artifacts on our data, we also performed the same analyses using motion, white matter (WM) and cerebrospinal fluid (CSF) as covariates. We show the difference between the two analyses in Figure 4. As it can be seen, differences were minimal, therefore confirming that the nuisances were not biasing significantly our results.

Bottom Line: One hypothesis is that the traditional analysis of pain-related cerebral responses, by relying on the correlation of a predictor and the canonical hemodynamic response function (HRF)- the general linear model (GLM)- may under-detect the activity of those areas involved in stimulus processing that do not present a canonical HRF.With this method we were able to evidence the involvement of several brain regions that are not usually found when using predictor-based analysis.These areas are synchronized during the administration of mechanical punctate stimuli and are characterized by a BOLD response different from the canonical HRF.

View Article: PubMed Central - PubMed

Affiliation: GCS fMRI, Koelliker Hospital and Department of Psychology, University of Turin Turin, Italy ; Department of Psychology, University of Turin Turin, Italy.

ABSTRACT
Pain is a complex experience that is thought to emerge from the activity of multiple brain areas, some of which are inconsistently detected using traditional fMRI analysis. One hypothesis is that the traditional analysis of pain-related cerebral responses, by relying on the correlation of a predictor and the canonical hemodynamic response function (HRF)- the general linear model (GLM)- may under-detect the activity of those areas involved in stimulus processing that do not present a canonical HRF. In this study, we employed an innovative data-driven processing approach- an inter-run synchronization (IRS) analysis- that has the advantage of not establishing any pre-determined predictor definition. With this method we were able to evidence the involvement of several brain regions that are not usually found when using predictor-based analysis. These areas are synchronized during the administration of mechanical punctate stimuli and are characterized by a BOLD response different from the canonical HRF. This finding opens to new approaches in the study of pain imaging.

No MeSH data available.


Related in: MedlinePlus