Brief Overview of Functional Imaging Principles

Abstract : Functional imaging enables to detect and to localize brain areas specifically involved in networks subserving a given mental activity. The two main techniques used in routine consist in functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). We will focus on fMRI. fMRI is based on the local and transient increase of blood oxygenation in the cortical and deep nuclear microvasculature, caused by neuronal activation. This hemodynamic phenomenon called blood-oxygenation level-dependent (BOLD) response only indirectly reflects the amount of neuronal activity (Raichle ME, Proc Natl Acad Sci USA 93:765–772, 1998). This local blood hyperoxygenation produces microscopic magnetic field alterations measurable by appropriate T2*-weighted MRI sequence. A complex image and statistical post-processing is then applied to raw data to generate final “brain activation maps.” These functional maps can be acquired during active stimulation protocols, or “at rest” (resting-state functional connectivity, rsfMRI). fMRI and rsfMRI resort to different algorithmic processing: linear model-related algorithms and nonlinear, model-free, data-driven algorithms, respectively, even if these latter methods can also be applied to fMRI data. This functional connectivity can be complemented by effective connectivity which seeks causality relationships between brain areas participating to a same circuit. Finally, advanced algorithms can also explore the topology of the neural networks and provide new and richer graph-theoretic related information, and machine learning will improve normal and aberrant structural and functional brain pattern recognition and classification.
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Christophe Habas, Giovanni de Marco. Brief Overview of Functional Imaging Principles. The Neuroimaging of Brain Diseases, Springer; Springer International Publishing, pp.27-58, 2018, ⟨10.1007/978-3-319-78926-2_2 ⟩. ⟨hal-02310399⟩

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