disorders of consciousness; FDG-PET; fMRI; ICA; metabolism; resting state
Abstract :
[en] 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 possi-
bility of creating functional MRI activity maps, which could estimate the rela-
tive 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 recogniz-
ing individual networks of independent component selection in functional mag-
netic 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 neu-
ronality 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 q = 0.75 0.05 for healthy controls and q = 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.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others Neurology
Author, co-author :
Soddu, Andrea ✱; Université de Liège > Centre de recherches du cyclotron
Gomez, Francisco ✱
Heine, Lizette ; Université de Liège > Centre de recherches du cyclotron
Di Perri, Carol ; Université de Liège > Centre de recherches du cyclotron
Bahri, Mohamed Ali ; Université de Liège > Centre de recherches du cyclotron
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