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Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness.

Soddu A, Gómez F, Heine L, Di Perri C, Bahri MA, Voss HU, Bruno MA, Vanhaudenhuyse A, Phillips C, Demertzi A, Chatelle C, Schrouff J, Thibaut A, Charland-Verville V, Noirhomme Q, Salmon E, Tshibanda JF, Schiff ND, Laureys S - Brain Behav (2015)

Bottom Line: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism.It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics & Astronomy, Brain and Mind Institute Western University London Ontario Canada.

ABSTRACT

Introduction: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.

Objective: We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.

Methods: We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients.

Results: The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls.

Conclusions: The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

No MeSH data available.


Related in: MedlinePlus

Scatter plots for all the 16 healthy controls showing the correlation between the FDG‐PET after partial volume correction versus the fMRI‐total neuronal activity for voxels belonging to gray matter. Solid line indicates the best linear fit to the data and on the upper left corner of each scatter plot the linear correlation value is reported.
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brb3424-fig-0002: Scatter plots for all the 16 healthy controls showing the correlation between the FDG‐PET after partial volume correction versus the fMRI‐total neuronal activity for voxels belonging to gray matter. Solid line indicates the best linear fit to the data and on the upper left corner of each scatter plot the linear correlation value is reported.

Mentions: We correlated the fMRI total neuronal activity maps (Eq. (1) in the methods) with the corresponding FDG‐PET metabolic activity maps for all voxels belonging to gray matter. We found for controls a significant (P < 0.001) correlation for all voxels of ρ = 0.75 ± 0.05, within the range 0.63–0.83 (see Fig. 2 for the scatter plots reporting FDG‐PET metabolic vs. fMRI total neuronal activity in healthy controls), for VS/UWS patients a significant (P < 0.001) correlation of ρ = 0.58 ± 0.09, within the range 0.45–0.76, and for LIS patients a significant (P = 0.001) correlation of ρ = 0.67 ± 0.03 ranging from 0.63 to 0.70 (see Figures S1, S2 for the scatter plots reporting FDG‐PET metabolic vs. fMRI total neuronal activity in, respectively, VS/UWS and LIS patients). Correlations between healthy controls and VS/UWS patients were significantly different (P < 0.001), the LIS patients group was significantly different from controls (P = 0.006), and was not significantly different from VS/UWS (P = 0.10). When calculating correlations for all voxels belonging to gray matter using FDG‐PET without performing any partial volume correction we obtained for healthy controls, VS/UWS and LIS, respectively, ρ = 0.74 ± 0.05, ρ = 0.59 ± 0.09, and ρ = 0.67 ± 0.03, which are not significantly different from the correspondent values when performing partial value correction (P = 0.71, P = 0.80 and P = 0.83). More relevant instead was the smoothing we used. Correlations between FDG‐PET partial volume corrected and fMRI total neuronal maps for the smoothing of 8 and 12 mm were, respectively, for healthy controls (ρ = 0.67 ± 0.05, ρ = 0.72 ± 0.05), VS/UWS (ρ = 0.50 ± 0.09, ρ = 0.55 ± 0.09) and LIS (ρ = 0.60 ± 0.03, ρ = 0.65 ± 0.03) with P values (P < 0.001, P = 0.1), (P = 0.048, P = 0.43) and (P = 0.021, P = 0.36) when compared to the smoothing of 16 mm.


Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness.

Soddu A, Gómez F, Heine L, Di Perri C, Bahri MA, Voss HU, Bruno MA, Vanhaudenhuyse A, Phillips C, Demertzi A, Chatelle C, Schrouff J, Thibaut A, Charland-Verville V, Noirhomme Q, Salmon E, Tshibanda JF, Schiff ND, Laureys S - Brain Behav (2015)

Scatter plots for all the 16 healthy controls showing the correlation between the FDG‐PET after partial volume correction versus the fMRI‐total neuronal activity for voxels belonging to gray matter. Solid line indicates the best linear fit to the data and on the upper left corner of each scatter plot the linear correlation value is reported.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834945&req=5

brb3424-fig-0002: Scatter plots for all the 16 healthy controls showing the correlation between the FDG‐PET after partial volume correction versus the fMRI‐total neuronal activity for voxels belonging to gray matter. Solid line indicates the best linear fit to the data and on the upper left corner of each scatter plot the linear correlation value is reported.
Mentions: We correlated the fMRI total neuronal activity maps (Eq. (1) in the methods) with the corresponding FDG‐PET metabolic activity maps for all voxels belonging to gray matter. We found for controls a significant (P < 0.001) correlation for all voxels of ρ = 0.75 ± 0.05, within the range 0.63–0.83 (see Fig. 2 for the scatter plots reporting FDG‐PET metabolic vs. fMRI total neuronal activity in healthy controls), for VS/UWS patients a significant (P < 0.001) correlation of ρ = 0.58 ± 0.09, within the range 0.45–0.76, and for LIS patients a significant (P = 0.001) correlation of ρ = 0.67 ± 0.03 ranging from 0.63 to 0.70 (see Figures S1, S2 for the scatter plots reporting FDG‐PET metabolic vs. fMRI total neuronal activity in, respectively, VS/UWS and LIS patients). Correlations between healthy controls and VS/UWS patients were significantly different (P < 0.001), the LIS patients group was significantly different from controls (P = 0.006), and was not significantly different from VS/UWS (P = 0.10). When calculating correlations for all voxels belonging to gray matter using FDG‐PET without performing any partial volume correction we obtained for healthy controls, VS/UWS and LIS, respectively, ρ = 0.74 ± 0.05, ρ = 0.59 ± 0.09, and ρ = 0.67 ± 0.03, which are not significantly different from the correspondent values when performing partial value correction (P = 0.71, P = 0.80 and P = 0.83). More relevant instead was the smoothing we used. Correlations between FDG‐PET partial volume corrected and fMRI total neuronal maps for the smoothing of 8 and 12 mm were, respectively, for healthy controls (ρ = 0.67 ± 0.05, ρ = 0.72 ± 0.05), VS/UWS (ρ = 0.50 ± 0.09, ρ = 0.55 ± 0.09) and LIS (ρ = 0.60 ± 0.03, ρ = 0.65 ± 0.03) with P values (P < 0.001, P = 0.1), (P = 0.048, P = 0.43) and (P = 0.021, P = 0.36) when compared to the smoothing of 16 mm.

Bottom Line: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism.It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics & Astronomy, Brain and Mind Institute Western University London Ontario Canada.

ABSTRACT

Introduction: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.

Objective: We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.

Methods: We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients.

Results: The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls.

Conclusions: The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

No MeSH data available.


Related in: MedlinePlus