Roebroeck Seth Valdes-SosaFigure 1: The neuronal, physiological and physical processes (top row) and variablesand parameters <strong>in</strong>volved (middle row) <strong>in</strong> the complex causal cha<strong>in</strong> of eventsthat leads to the formation of the fMRI signal. The bottom row lists somemathematical models of the sub-processes that play a role <strong>in</strong> the analysis andmodel<strong>in</strong>g of fMRI signals. See ma<strong>in</strong> text for further explanation.76
Causal analysis of fMRIthe action potential along the axon, and release of neurotransmitter substances <strong>in</strong>to thesynaptic cleft at arrival of an action potential at the synaptic term<strong>in</strong>al. There are manydifferent types of neurons <strong>in</strong> the mammalian bra<strong>in</strong> that express these processes <strong>in</strong> differentdegrees and ways. In addition, there are other cells, such as glia cells, that performimportant processes, some of them possibly directly relevant to computation or signal<strong>in</strong>g.As expla<strong>in</strong>ed below, the fMRI signal is sensitive to the local oxidative metabolism<strong>in</strong> the bra<strong>in</strong>. This means that, <strong>in</strong>directly, it ma<strong>in</strong>ly reflects the most energy consum<strong>in</strong>gof the neuronal processes. In primates, post-synaptic processes account for the greatmajority (about 75%) of the metabolic costs of neuronal signal<strong>in</strong>g events (Attwell andIadecola, 2002). Indeed, the greater sensitivity of fMRI to post-synaptic activity, ratherthan axon generation and propagation (‘spik<strong>in</strong>g’), has been experimentally verified. For<strong>in</strong>stance, <strong>in</strong> a simultaneous <strong>in</strong>vasive electrophysiology and fMRI measurement <strong>in</strong> theprimate, Logothetis and colleagues (Logothetis et al., 2001) found the fMRI signal tobe more correlated to the mean Local Field Potential (LFP) of the electrophysiologicalsignal, known to reflect post-synaptic graded potentials, than to high-frequency andmulti-unit activity, known to reflect spik<strong>in</strong>g. In another study it was shown that, bysuppress<strong>in</strong>g action potentials while keep<strong>in</strong>g LFP responses <strong>in</strong>tact by <strong>in</strong>ject<strong>in</strong>g a seroton<strong>in</strong>agonist, the fMRI response rema<strong>in</strong>ed <strong>in</strong>tact, aga<strong>in</strong> suggest<strong>in</strong>g that LFP is a betterpredictor for fMRI activity (Rauch et al., 2008). These results confirmed earlier resultsobta<strong>in</strong>ed on the cerebellum of rats (Thomsen et al., 2004).Neuronal activity, dynamics and computation can be modeled at a different levelsof abstraction, <strong>in</strong>clud<strong>in</strong>g the macroscopic (whole bra<strong>in</strong> areas), mesoscopic (sub-areasto cortical columns) and microscopic level (<strong>in</strong>dividual neurons or groups of these).The levels most relevant to model<strong>in</strong>g fMRI signals are at the macro- and mesoscopiclevels. Macroscopic models used to represent considerable expanses of gray matter tissueor sub-cortical structures as Regions Of Interest (ROIs) prom<strong>in</strong>ently <strong>in</strong>clude s<strong>in</strong>glevariable determ<strong>in</strong>istic (Friston et al., 2003) or stochastic (autoregressive; Penny et al.,2005; Roebroeck et al., 2005; Valdes-Sosa et al., 2005b) exponential activity decaymodels. Although the simplicity of such models entail a large degree of abstraction<strong>in</strong> represent<strong>in</strong>g neuronal activity dynamics, their modest complexity is generally wellmatched to the limited temporal resolution available <strong>in</strong> fMRI. Nonetheless, more complexmulti-state neuronal dynamics models have been <strong>in</strong>vestigated <strong>in</strong> the context offMRI signal generation. These <strong>in</strong>clude the 2 state variable Wilson-Cowan model (Marreiroset al., 2008), with one excitatory and one <strong>in</strong>hibitory sub-population per ROI andthe 3 state variable Jansen-Rit model with a pyramidal excitatory output populationand an <strong>in</strong>hibitory and excitatory <strong>in</strong>terneuron population, particularly <strong>in</strong> the model<strong>in</strong>g ofsimultaneously acquired fMRI and EEG (Valdes-Sosa et al., 2009).The physiology and physics of the fMRI signal is most easily expla<strong>in</strong>ed by start<strong>in</strong>gwith the physics. We will give a brief overview here and refer to more dedicatedoverviews (Haacke et al., 1999; Uludag et al., 2005) for extended treatment. The hallmarkof Magnetic Resonance (MR) spectroscopy and imag<strong>in</strong>g is the use of the resonancefrequency of magnetized nuclei possess<strong>in</strong>g a magnetic moment, mostly protons(hydrogen nuclei, 1H), called ‘sp<strong>in</strong>s’. Radiofrequency antennas (RF coils) can measure77
Change language