Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic data

Université Pierre et Marie Curie-Paris 6, Centre de Recherche de l’institut du Cerveau et de la Moelle épinière, UMR-S975, 75651 Paris, France.
Reviews in the neurosciences (Impact Factor: 3.33). 02/2012; 23(1):85-95. DOI: 10.1515/rns.2011.056
Source: PubMed


Cognitive functions involve not only cortical but also subcortical structures. Subcortical sources, however, contribute very little to magnetoencephalographic (MEG) and electroencephalographic (EEG) signals because they are far from external sensors and their neural architectonic organization often makes them electromagnetically silent. Estimating the activity of deep sources from MEG and EEG (M/EEG) data is thus a challenging issue. Here, we review the influence of geometric parameters (location/orientation) on M/EEG signals produced by the main deep brain structures (amygdalo-hippocampal complex, thalamus and some basal ganglia). We then discuss several methods that have been utilized to solve the issues and localize or quantify the M/EEG contribution from deep neural currents. These methods rely on realistic forward models of subcortical regions or on introducing strong dynamical priors on inverse solutions that are based on biologically plausible neural models, such as those used in dynamic causal modeling (DCM) for M/EEG.

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Available from: Yohan Attal, Jan 20, 2014
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    • "In a series of MEG-studies, Tesche (1996) for example found evidence for thalamic responses to median nerve stimulation that resembled the pattern of in-depth thalamic electrode recordings reported from another group (Albe-Fessard et al., 1986). Attal et al. (2012) recently reviewed the state of the art of non-invasive detection of deepbrain activity and underpinned the need for sophisticated head models that take into account the unique cytoarchitecture of subcortical structures. They further suggested to assess subcortical activity by using biodynamical models. "

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    • "This is a fundamental advantage of DCM over classic electromagnetic reconstruction procedures that do not have a forward model of coupling among sources. In short, the validity of DCM is not compromised by the ability to record high signal-to-noise ratio data from all sources present in the model (Attal et al., 2012). Indeed , others have successfully used DCM to emulate hidden or silent sources (i.e., sources that do not contribute to the activity recorded at the scalp) in application to language models (David et al., 2011). "
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