Biomedical subjects

Habib Benali

Publications and source records attributed to Habib Benali.

4 recordsLinked to original sources

PubMed2003

Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information.

In BOLD fMRI data analysis, robust and accurate estimation of the Hemodynamic Response Function (HRF) is still under investigation. Parametric methods assume the shape of the HRF to be known and constant throughout the brain, whereas non-parametric methods mostly rely on artificially increasing the signal-to-noise ratio. We extend and develop a previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework. A general hypothesis test is also proposed, allowing to take advantage of the knowledge gained regarding the HRF to perform activation detection. The performances of the method are then evaluated by simulation. Great improvement is shown compared to the Maximum-Likelihood estimate in terms of estimation error, variance, and bias. Robustness of the estimators with regard to the actual noise structure or level, as well as the stimulus sequence, is also proven. Lastly, fMRI data with an event-related paradigm are analyzed. As suspected, the regions selected from highly discriminating activation maps resulting from the method exhibit a certain inter-regional homogeneity in term of HRF shape, as well as noticeable inter-regional differences.

Adult↗

PubMed2002

Multivariate model specification for fMRI data.

We present a general method-denoted MoDef-to help specify (or define) the model used to analyze brain imaging data. This method is based on the use of the multivariate linear model on a training data set. We show that when the a priori knowledge about the expected brain response is not too precise, the method allows for the specification of a model that yields a better sensitivity in the statistical results. This obviously relies on the validity of the a priori information, in our case the representativity of the training set, an issue addressed using a cross-validation technique. We propose a fast implementation that allows the use of the method on large data sets as found with functional Magnetic Resonance Images. An example of application is given on an experimental fMRI data set that includes nine subjects who performed a mental computation task. Results show that the method increases the statistical sensitivity of fMRI analyses.

Brain↗

PubMed2002

ICA of fMRI group study data.

This paper proposes to extend independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data from single subjects to simultaneous analysis of data from a group of subjects. This results in a set of time courses which are common to the whole group, together with an individual spatial response pattern for each of the subjects in the group. The method is illustrated using data from two fMRI experiments. The results show that: (a) ICA is capable of extracting nontrivial task related components without any a priori information about the fMRI experiment; (b) in analysis of group data, ICA identifies components common to the whole group as well as components manifested in single subjects only.

Brain↗

PubMed2002

Modeling of pathophysiological coupling between brain electrical activation, energy metabolism and hemodynamics: insights for the interpretation of intracerebral tumor imaging.

Gliomas can display marked changes in the concentrations of energy metabolism molecules such as creatine (Cr), phosphocreatine (PCr) and lactate, as measured using magnetic resonance spectroscopy (MRS). Moreover, the BOLD (blood oxygen level dependent) contrast enhancement in functional magnetic resonance imaging (fMRI) can be reduced or missing within or near gliomas, while neural activity is not significantly reduced (so-called neurovascular decoupling), so that the location of functionally eloquent areas using fMRI can be erroneous. In this paper, we adapt a previously developed model of the coupling between neural activation, energy metabolism and hemodynamics, by including the venous dilatation "Balloon model" of Buxton and Frank. We show that decreasing the cerebral blood flow (CBF) baseline value, or the CBF increase fraction, results in a decrease of the BOLD signal and an increase of the lactate peak during a sustained activation. Baseline lactate and PCr levels are not significantly affected by CBF baseline reduction, but are altered even by a moderate decrease of mitochondrial respiration. Decreasing the total Cr and PCr concentration reduces the BOLD signal after the initial overshoot. In conclusion, we suggest that the coupled use of BOLD fMRI and MRS could contribute to a better understanding of the neurovascular and metabolic decoupling in gliomas.

Adenosine Triphosphate↗