Introduction to functional brain imaging
Accessing brain activity non-invasively using neuroimaging techniques has been possible for about two decades and has continued to thrive from the technical and methodological standpoints (K. J. Friston, 2009). With the ubiquitous availability of magnetic resonance imaging (MRI) scanners in major hospitals and research centers, functional-MRI (fMRI) has certainly become the modality-of-choice to approach the human brain in action. A well-documented and thoroughly-discussed limitation of fMRI however, sits in the very physiological origins of the signals accessible to the analysis. Indeed, fMRI is essentially sensitive to local fluctuations in blood oxygen levels and flow, which connexion to cerebral activity is the object of very active scientific investigations and sometimes, controversies (Logothetis & Pfeuffer, 2004, Logothetis & Wandell, 2004, Eijsden, Hyder, Rothman, & Shulman, 2009). A more fundamental limitation of fMRI and metabolic techniques such as Positron Emission Tomography (PET) is the lack of temporal resolution.
In essence, the physiological changes captured by these techniques fluctuate within a typical time scale of several hundreds of milliseconds at best, which makes them excellent at mapping the regions involved in task performance or resting-states (Fox & Raichle, 2007), but incapable of resolving the flow of rapid brain activity that unfolds with time. Metaphorically speaking, metabolic and hemodynamic techniques perform as very sensitive cameras that are able to capture low-intensity signals using long aperture durations, hence a sluggish temporal resolution. This basic limitation has become salient as new neuroscience questions emerge to investigate the brain as an ensemble of complex networks that form, reshape and flush information dynamically (Varela, Lachaux, Rodriguez, & Martinerie, 2001, Sergent & Dehaene, 2004, Werner, 2007).
An additional, though seemingly minor, limitation of hemodynamic (i.e. MRI-based) modalities consist in their operational environment: most scanners are installed in hospitals, with typically limited access time but more importantly, necessitate that subjects lie supine in a narrow tunnel, with loud noises generated by the acquisition processes. Such non-ecological environment is certainly detrimental to the subject’s comfort and therefore, limits the possibilities in terms of stimulus presentation and real-time interaction with participants, which are central issues in e.g., Social Neuroscience studies or research and clinical sessions with children.
These pages therefore describe how Electroencephalography (EEG) and Magnetoencephalography (MEG) offer complementary alternatives to typical neuroimaging studies in that respect. We will briefly review the basic, though very rich, methods of sensor data analysis, which focus of the chronometry of so-called brain events. We will further emphasize how MEG and EEG may be utilized as neuroimaging techniques, that is, how they are capable to map dynamic brain activity and functional connectivity with fair spatial resolution and unique rapid time scales. EEG recordings have been made possible in the MRI environment, therefore leading to multimodal data acquisition and analysis (Laufs, Daunizeau, Carmichael, & Kleinschmidt, 2008). This has brought up interesting discussions and results on e.g., rapid phenomena such as epileptiform events and the electrophysiological counterpart of BOLD resting-state fluctuations (Mantini, Perrucci, Gratta, Romani, & Corbetta, 2007). MEG and EEG data acquired with high-density sensor arrays also stand by themselves as functional neuroimaging techniques: this is the realm of electromagnetic brain mapping (Salmelin & Baillet, 2009). It is indeed interesting to note that MEG instruments are being delivered to prominent functional neuroimaging clinical and research centers who are willing to expand their investigations beyond the static, functional cartography of the brain. These pages offer a pragmatic review of this rapidly evolving field.