Rev. Neurosci., Vol. 23(1): 85–95, 2012 • Copyright © by Walter de Gruyter • Berlin • Boston. DOI 10.1515/RNS.2011.056

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

Yohan Attal 1 – 4 , Burkhard Maess 5 , Angela Friederici 5 and Olivier David 6 – 8, *

1 Universit é Pierre et Marie Curie-Paris 6 , Centre de Recherche de l ’ institut du Cerveau et de la Moelle é pini è re, UMR-S975, 75651 Paris , France 2 Inserm , U975, 75651 Paris , France 3 CNRS , UMR7225, 75651 Paris , France 4 ICM – Institut du Cerveau et de la Mo ë lle é pini è re , 75651 Paris , France 5 Max Planck Institute for Human Cognitive and Brain Sciences , Department of Neuropsychology, 04103 Leipzig , Germany 6 Brain Function and Neuromodulation , Joseph Fourier University, 38042 Grenoble , France 7 Grenoble Institut des Neurosciences , Chemin Fortun é Ferrini – B â t EJ Safra – CHU, 38700 La Tronche , France 8 Neuroradiology Department and MRI Unit , University Hospital, 38042 Grenoble , France

* Corresponding author e-mail: odavid@ujf-grenoble.fr

Abstract

Cognitive functions involve not only cortical but also sub-cortical structures. Subcortical sources, however, contribute very little to magnetoencephalographic (MEG) and electro-encephalographic (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 infl uence 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 contribu-tion from deep neural currents. These methods rely on realis-tic 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.

Keywords: deep brain activity; dynamic causal modeling; electroencephalography; head models; inverse models; magnetoencephalography.

Introduction

Cognitive processes not only rely on cortical but also on subcortical brain systems. The recording of subcortical activity by means of magnetoencephalography (MEG) and electroencephalography (EEG), which record electromag-netic activity on the scalp, remains a challenge (Hamalainen et al. , 1993 ; Nunez and Srinivasan , 2005 ). Source localization techniques have shown some consistency in localizing changes in electrical activity in the neocortex (Baillet et al. , 2001 ). However, reliable detection of activity coming from deep brain structures is still an open question because its signal-to-noise ratio (SNR) in scalp recordings is marginal (Guy et al. , 1993 ; Tesche , 1996a ; Mikuni et al. , 1997 ; Shigeto et al. , 2002 ; Nunez and Srinivasan , 2005 ; Stephen et al. , 2005 ; Attal et al. , 2009 ; Riggs et al. , 2009 ). Spatial sensitivity of MEG and EEG (M/EEG) indeed depends on instrumental and biological parameters, such as the distance between neuronal populations and M/EEG sensors and the complex cellular architectures of deeper sources (Hillebrand and Barnes , 2002 ; Stephen et al. , 2005 ; Attal et al. , 2009 ). Deep structures have thus long been considered as producing very limited electro-magnetic fi elds on the scalp.

Nevertheless, quantifying the M/EEG contribution from deep neural currents is crucial for studying their implica-tions in numerous brain processes (e.g ., language, action, motor, emotion) and related disorders (e.g ., stroke, epilepsy, Alzheimer ’ s, Parkinson ’ s or Huntington ’ s disease). It is there-fore important to carefully assess the effects of source location and orientation, geometrical shapes and neural architectonic organization. In contrast to modeling of the neocortex where a dipolar model is generally used to summarize local cortical current densities, biophysical modeling of each deep struc-ture is specifi c because of important interregional differences (Stephen et al. , 2005 ; Attal et al. , 2009 ; Riggs et al. , 2009 ). Not only accurate modeling of the biophysics of M/EEG, but also the experimental design, and the number of trials and subjects can play a signifi cant role in improving the SNR, for instance, in reducing the neocortical contribution that could be overlapped with deep activity (Quraan et al. , 2011 ).

Although activity in subcortical regions may not be recorded directly in M/EEG, the role of these regions is impor-tant in the measured dynamics of necortical regions because of strong subcortical-cortical loops. It has recently been pro-posed to use such effective connectivity to infer subcortical activity from other recorded regions by means of biophysical modeling of long range subcortical/cortical networks, such as the ones proposed in dynamic causal modeling (DCM) for

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86 Y. Attal et al.

M/EEG (David et al. , 2006 , 2011 ). In this approach, subcorti-cal activity is derived from latent variables of dynamic mod-els of whole brain activity. It thus constitutes a different and complementary solution to deep source estimation than the more classical approach consisting of improving head models of M/EEG data.

In this review, we build on the solutions proposed so far for the estimation of deep brain activity using M/EEG. First, we present the basic physiology of M/EEG data, with a focus on deep structures. Second, we move to the state of the art of deep brain activity reconstruction from head models. Finally, we develop new ways of solving this issue using biophysical models of DCM.

Noninvasive recordings of subcortical

electric activity

M/EEG are noninvasive techniques measuring electromag-netic signatures of changes in neuronal ionic currents outside of the head on a timescale of milliseconds. These measure-ments strongly depend on features of neuronal cells (e.g., location, orientation, density, type) and on the global geo-metrical shape of structures containing them. Characterizing the relationships between these features and M/EEG signal properties is particularly important to better quantify the sub-cortical contribution to scalp recordings. In the following, we fi rst briefl y describe common assumptions on the neocortical origin of the M/EEG signals (for more details, see Freeman , 1975 ; Hamalainen et al. , 1993 ; Nunez and Srinivasan , 2005 ) and, second, we provide a detailed discussion of the signal origins from deep structures.

Origin of the M/EEG signals in the neocortex

The general consensus is that M/EEG measurements result from changes in the membrane potential of neurons – particularly in relation to postsynaptic potentials (PSPs) occurring at the neural apical dendritic tree of principal cells (Murakami and Okada , 2006 ). PSPs are created by ion chan-nels opening following the arrival of action potentials from afferent neurons. This produces intracellular currents trav-elling through the dendrite, called the primary current, and extracellular return (or secondary or volume) currents that respect the local conservation of electric charges. Therefore, PSPs are usually modeled by an equivalent current dipole (ECD) oriented along the dendrite with a dipolar moment q . This dipolar moment is not suffi cient to be detectable at the level of a single neuron ( ∼ 0.02 pAm) (Hamalainen et al. , 1993 ). M/EEG signals are necessarily generated by the synchronous activity of tens of thousands of principal cells that can be modeled by an ECD with a dipolar moment, Q , corresponding to the vector sum of q values of synchronous neurons. The ECD is oriented along the direction originating from the cell soma and pointing towards the principal axis of the dendritic tree. Therefore, resultant dipolar moment Q can be high only if the dendritic trees of neurons are somewhat parallel. This is the reason why the shape of the dendritic tree

of neurons is a critical feature of M/EEG generators, as we will see in the next section.

In standard M/EEG source modeling, pyramidal cells that are organized into neocortical macrocolumns are thus sup-posed to support the primary current. From electrophysio-logical measurements, it has been proposed that detectable dipolar moment densities are in a range of 25 up to 250 pAm/mm 2 (Freeman , 1975 ; Kraut et al. , 1985 ; Hamalainen et al. , 1993 ). Experimental data obtained in animals showed den-sities at 400 pAm/mm 2 in the fi rst somatosensory neocortex in pig (Okada et al. , 1996 ) and at 800 pAm/mm 2 in guinea pig hippocampal slices (Okada et al. , 1997 ). Assuming a dipolar moment in the order of 10 nAm for measurable cortical gen-erators in humans, this would correspond to 40 mm 2 of active cortex over an effective layer thickness estimated at 1 mm (Chapman et al. , 1984 ). Other studies have reported approxi-mated thickness values between 0.2 and 0.4 mm (Kuffl er et al. , 1984 ; Hari , 1990 ; Okada et al. , 1997 ). In these cases, the activated area would be up to fi ve times larger to reach similar dipolar moments. Recently, by modeling the enve-lopes of pyramidal cells of layers II/III/V, a dipolar moment Q between 0.29 and 0.90 pAm was reported (Murakami and Okada , 2006 ) – 15–45 times higher than the estimation of 0.02 pAm fi rst proposed by Hamalainen et al. (1993) .

In general, brain structures can be classifi ed as ‘ open ’ or ‘ closed ’ according to the confi guration of the electromagnetic fi eld they produce (De No , 1947 ). The neocortex is typically an open structure: pyramidal cells are large and ‘ open-fi eld ’ because of their global cell morphology, which is essentially longitudinal and perpendicular to the surface of the cortical sheet. The PSP vector sum along the apical dendritic tree is thus strong. In contrast, dendrites of stellate cells are consid-ered to have ‘ closed-fi eld ’ confi gurations because they form a radial arborescence around the cell body and have their electrical fi elds confi ned within their volume. Several stud-ies reported their quasi-null contribution to M/EEG signals at large distances (Okada , 1982 ; Nunez and Srinivasan , 2005 ); therefore this type of cell is usually believed to be undetect-able by M/EEG. However, using simulations, Murakami and Okada (2006) showed an unexpected result regarding the magnitude of dipolar currents produced by cortical spiny stellate cells. The amplitude of the PSP dipolar moment Q of a stellate cell was as high as 0.27 pAm, which is in the same order of magnitude as that produced by a pyramidal cell. However, because the orientation of dendrites of spiny stel-late cells is variable, the summed Q may be diminished at the level of the neuronal population.

Neural architectonic organization of deep structures

Description of the neural architecture is important to best assess the ability of M/EEG to detect deep brain activity and to develop realistic models of deep structures that may differ signifi cantly from standard models of the neocortical sheet. In the following, we will focus on the amygdalo-hippocampal complex and on fi ve of the main central gray nuclei (Figure 1 ): striatum (putamen and caudate nucleus), thalamus, retic-ular perithalamic nucleus (RPN), lateral geniculate nucleus

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Models of subcortical regions for MEG/EEG 87

(LGN) and external pallidum (EGP). Table 1 summarizes important features of these structures in relation to scalp recordings.

Amygdalo-hippocampal complex The hippocampus is a folded cortical structure located in the medial temporal lobe (Figure 1 ), which has a distinctive intimate cellular architecture from the rest of the cortex (Duvernoy , 2005 ). It is an archeo-cortex (or three-layered cortex) unlike the neo-cortex (six-layered cortex), with a transition from the neocortex to the hippocampus through the parahippocampal and subicular regions. Schematically, the hippocampus contains Ammon ’ s horn and the dentate gyrus, two open-fi eld structures that are interlocked. Ammon ’ s horn can be decomposed into four parts (CA1 to CA4) from the subiculum to the dentate gyrus. Neurons of Ammonian fi elds (mainly pyramidal cells) have parallel dendritic trees, similar to those of the cerebral cortex (also oriented perpendicularly to the hippocampal envelope). This suggests that a reasonable active portion of these subfi elds can generate large magnetic fi elds on the scalp. Ganular cells of the dentate gyrus have a smaller size than pyramidal neurons but also have an oriented dendritic tree. Moreover, the dentate gyrus has a high neural density (more than ten times as numerous as pyramidal neurons of Ammon ’ s horn) and also must be taken into account in the hippocampus contribution to M/EEG signals.

The complex bilaminar geometry of the hippocam-pus (Andersen et al. , 1969 ) led to the common assumption that hippocampal activations could contribute very little to M/EEG signals. This hypothesis was challenged by Amaral and Witter (1989) , where the authors concluded that ‘ it is heuristically most reasonable to consider the hippocampal for-mation as a three-dimensional cortical region with important

information processing taking place in both transverse and longitudinal axes ’ . Other authors differentiated the activity of each hippocampal subfi eld [defi ned at 1.5T magnetic reso-nance imaging (MRI)] in order to quantify the level of MEG cancellation of concurrent activation (Stephen et al. , 2005 ). They found less cancellation than expected; however, these subfi elds usually cannot be precisely identifi ed on standard 1.5T and 3T MRI, but new high fi eld 7T MRI offers the excit-ing prospect of improving noninvasive segmentation of the human hippocampus (Van Leemput et al. , 2009 ; Yushkevich et al. , 2009 ).

The amygdala is a complex of gray nuclei located in front of, and above, the hippocampus (Sah et al. , 2003 ). Its neural density is fi ve to six times greater than neocortical neural den-sity (Dumas et al. , 2011 ). Nuclei of amygdala are functionally related and can be separated into three groups: the basolat-eral group, the centromedial group and the cortical group (for more details, see LeDoux , 2007 ; Whalen and Phelps , 2009 ). The basolateral group, which contains more than the half of amygdala neurons, is mostly composed of pyramidal cells. Overall, one can expect a signifi cant contribution from the amygdala neurons to the scalp data, even if cellular organiza-tion is not as laminar as in the neocortical macrocolumns.

Basal ganglia and thalamus Functionally, the basal ganglia and thalamus can be divided into three distinct territories (sensorimotor, associative, limbic) which are defi ned by subcortical-cortical connectivity with the different functionally specialized regions of the cortex (Alexander and Crutcher , 1990 ). The basal ganglia are composed of various cell types and cell densities. In terms of dendritic morphology, neurons of the striatum belong to several neural species that are morphologically very different (Yelnik et al. , 1991 ). The large majority (96 % ) is constituted of spiny stellate cells that contain dendritic spines connected by dopaminergic synapses to cortical afferents from functional territories (Smith and Bolam , 1990 ). Neural thalamic geometry is close to the striatum (Yelnik , 2006 ). The thalamus is mainly composed of stellate cells which are ten times less numerous than in the

Figure 1 DBA model of cortical and subcortical structures. Tessellated envelopes of left cortical hemisphere and right deep structures: hippocampus (red), amygdala (yellow), EGP (light blue), thalamus (orange), LGN (dark blue), RPN (green), putamen (purple). Adapted from Yelnik (2006) and Attal et al. (2009) .

Table 1 Global characteristics and properties of the cortical and subcortical structures of left hemisphere.

Structure Surface (cm 2 ) Cell type

Cell density

Current density(nAm/mm 2 /mm 3 )Volume (cm 3 )

Cortex 750 O / 0.25 (to 0.4)Hippocampus 15 O 2.5 0.4 (to 0.8)Amygdala 1 O 6 up to 1.5Thalamus 8 C 1/10 0.025LGN 0.2 O 1 0.25EGP 1.5 O 1/100 0.0025Putamen 9 C 1 0.25RPN 2 O 1/100 0.0025

Cell density ratio is an average ratio for each subcortical structure compared with the average cortical cell density. C, ‘ closed fi eld ’ cells; O, ‘ open fi eld ’ cells. Adapted from Attal et al. (2009) and Dumas et al. (2011) .

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88 Y. Attal et al.

striatum, and thus it is a ‘ closed-fi eld ’ structure. Neurons of the pallidum have a completely different geometry than that of stellate cells of the striatum and thalamus. They have very large, sparsely branched dendrites that form fl attened discs arranged perpendicularly to the afferent axons of the striatum (Yelnik et al. , 1984 ). The basal ganglia are thus characterized by important differences between the striatum and the pallidum in terms of neuronal geometry and density: there is one pallidal neuron for 100 striatal neurons and the volume of the striatum is 20 times larger than that of the pallidum (Yelnik , 2006 ). M/EEG recordings are thus much more likely to detect striatal than pallidal activity.

Subcortical source reconstruction:

state of the art

Estimation of the generators of M/EEG signals can be done by sequentially solving the forward problem and the inverse problem. The forward problem computes the magnetic fi eld B and the electric potential V owing to a particular ECD con-fi guration by solving the Maxwell equation under quasi-static approximation, throughout and outside of the head using real-istic or spherical head models (Sarvas , 1987 ; Nolte , 2003 ). A simple spherical homogenous head model is routinely used in most clinical and research applications to M/EEG source localization and could be suffi cient for MEG. Realistic head models, such as the boundary element method (BEM), fi nite element method (FEM) and fi nite difference method (FDM) (Johnson , 1997 ; Fuchs et al. , 2001, 2002 ) are spatially more accurate, especially for EEG, owing to their high sensitiv-ity to the diffusion of volume currents (Leahy et al. , 1998 ; Darvas et al. , 2005 ; Wolters et al. , 2006 ). The result is the so-called gain matrix composed by the contribution of each considered source of the model, as potential generators, to the external sensors. Finally, the inverse problem estimates the current sources that produced these signals (for a review, see Baillet et al. , 2001 ).

Forward models of subcortical regions

Because of the specifi c electrophysiological and neuroana-tomical properties of deep structures, our group has recently been developing realistic models of deep brain activity (DBA) using an imaging approach (Chupin et al. , 2002 ; Attal et al. , 2009 ; Dumas et al. , 2011 ). This section describes the DBA model.

The DBA model distributes ECD over neocortical and sub-cortical tessellations obtained from individual T1-weighted MRI sequences (Figure 1 ). Neocortex segmentation is obtained by the generic pipeline of BrainVisa software ( http://brainvisa.info ). The amygdalo-hippocampal complex is seg-mented using a fully automated method (Chupin et al. , 2007, 2009 ). Meshes of basal ganglia are obtained from a recent deformable atlas of basal ganglia and related structures com-bining detailed histological slices with the post mortem MRI of a specimen anatomy and an atlas warping procedure to the individual anatomy of subjects (Yelnik et al. , 2007 ; Bardinet

et al. , 2009 ). Electrophysiological knowledge is then intro-duced by specifying ECD location and orientation in a struc-ture-specifi c manner.

Because basal ganglia are mainly volumetric nuclei of gray matter, their large-scale electrophysiology cannot be approximated by distributing current dipoles on their surface. Instead, regular volume grids are fi tted within their surface envelopes to support a volume distribution of elementary cur-rent dipoles. For nuclei with oriented sources (EGP, RPN and LGN), dipoles are oriented along the principal axis of their respective surface envelope. The thalamus and striatum are essentially made of closed-fi eld cells (i.e., with no preferred source orientation), hence a current dipole is placed at each node of the inner volume grid with random orientation. The basolateral nucleus of the amygdala is mainly composed of pyramidal cells without preferential orientation and is there-fore modeled in the same way as the thalamus and striatum (Dumas et al. , 2011 ). The current model of the hippocampus is limited to its global external envelope, with ECD distrib-uted orthogonally to the local surface as for the neocortex, under the assumption that CA1, CA2 and CA3, which con-tain pyramidal neurons, are the main hippocampal generators. This model of the hippocampus is being extended to take into account granular cells in the dentate gyrus and the bilaminar shape of the hippocampus.

Figure 2 shows MEG (151 gradiometers, CTF Inc., Dorval, QC, Canada) grand-average sensitivity maps of neocorti-cal and deep sources using the DBA model of the Montreal Neurological Institute (MNI) brain template. A sensor-weighted overlapping sphere head model (Huang et al. , 1999 ) was used to compute the forward gain matrix with BrainStorm software (Tadel et al. , 2011 ) ( http://neuroimage.usc.edu/brainstorm/ ). Each bar of Figure 2 corresponds to the average root mean square (RMS) for all locations in the source space of each structure, computed as the sum of squares of the corresponding column of the forward solution. Source sensitivity maps were also plotted for the neocortex, the hippocampus and the putamen. As is well known in the case of the neocortex, MEG sensors are most sensitive to sources located in gyri (Hamalainen et al. , 1993 ; Hillebrand and Barnes , 2002 ). Sensitivity is dramatically lower for radial sources at the hippocampus edges and at the crests of neocor-tical gyri. Sensitivity also drops off radically with the distance from sensors between the neocortex and all deep structures. For example, the thalamus and LGN have the lowest sen-sitivity, mainly because of their central positions far away from sensors. The image of the putamen shows higher MEG sensitivity to the anterior part, mainly because of the nearest distance of anterior sources to sensors. Note that because the putamen has random ECD orientation, MEG sensitivity is het-erogeneous in that region. Weaker sensitivity of the temporal lobe as compared with other neocortical sources is most likely caused by head position of the MNI template in the CTF sys-tem (co-registered using fi ducials). Sources in the temporal lobe showed a relatively large distance to the sensors.

Figure 2 shows sensitivity to homogenous current densities, which is function of the chosen head model and the location and orientation of the sources. However, one should note that

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Models of subcortical regions for MEG/EEG 89

in actual data, M/EEG sensitivity has to be weighted by local changes in current densities and variability observed in deep regions greatly modulates M/EEG ability to detect deep gen-erators. For the hippocampus or amygdala, one could expect the currents, which are larger than in the neocortex, to greatly compensate for the depth of sources and the geometry of the neural architecture. Further improvements in MEG sensitiv-ity can be expected from newest MEG systems that combine numerous gradiometers and magnetometers. The imbalance between deep and external sources is also smaller for EEG than for MEG because of Maxwell ’ s laws.

Inverse models of subcortical regions

Forward modeling shows that M/EEG sensitivity to deep sources is small, but using inversion techniques, potentially suffi cient to try to estimate activity from scalp measurements, at least in some subcortical regions. Common inverse meth-ods can be decomposed into parametric approaches (dipole fi t), imaging approaches (distributed source models) and beamforming approaches (spatial fi lter) (for a review, see Baillet et al. , 2001 ; Mosher et al. , 2003 ).

Assuming a fi xed number of ECDs, the standard dipole fi t approach estimates ECD location, orientation and strength (Mosher et al. , 1992 ; Uutela et al. , 1998 ). Dipole fi tting usu-ally provides robust estimates. However, because the cost function is nonlinear, localization accuracy decreases for multiple active sources, especially when the assumed number

of dipoles differs from the true number (Wood and Wolpaw , 1982 ; Hari and Forss , 1999 ).

Imaging approaches distribute ECDs over the brain (con-sidered as a surface or a volume) and estimate their strength (and orientation if unconstrained a priori) (Baillet et al. , 2001 ). The minimum norm estimate (MNE) (Dale and Sereno , 1993 ; Hamalainen et al. , 1993 ; Wang , 1993 ) is widely used to esti-mate the source distribution with minimal energy (L2-norm). MNE is biased towards more superfi cial sources that have a higher forward fi eld (Figure 2 ), which makes detection of deep sources more diffi cult (Dale et al. , 2000 ; Pascual -Marqui et al., 2002 ). Depth-weighted solutions might improve sensi-tivity to deeper sources (see for a review Lin et al. , 2006 ). Although MNE leads to robust results, it also produces diffuse solutions that can lead to spurious activations, which is partic-ularly important at the level of deep sources. Other methods based on the minimum current estimate (MCE) (L1-norm) lead to more focal estimates (Matsuura and Okabe , 1995 ; Uutela et al. , 1999 ), but also need to be depth-weighted and suffer from unstable activation patterns. It is possible to mix L1- and L2-norms to get inverse solutions that take advantage of both approaches (Ou et al. , 2009 ).

Beamforming approaches perform spatial fi ltering of the data to create a weighted sum representing an estimate of source activity from a desired source location (Van Veen et al. , 1997 ). ECDs are positioned on a regular grid within the cortical envelope or in the brain volume to assess deeper source contributions. Because it is not constrained to cortical meshes, most studies on deep brain activity have used beam-forming. However, beamformer approaches assume that two brain areas are not activated coherently over long time scales, which might not necessarily be true within subcortical-corti-cal loops. This issue is often presented as pragmatically rea-sonable (Hillebrand et al. , 2005 ), but several studies have also proposed solutions to diminish the effects of temporal cor-relations of sources (Sekihara et al. , 2001 ; Dalal et al. , 2006 ; Cheyne et al. , 2007 ).

Several studies have used these techniques to report the ability of M/EEG to detect signals from deep structures, such as the amygdala, hippocampus or thalamus, with a strong increase of interest during the last decade (Okada et al. , 1983 ; Ribary et al. , 1991 ; Rogers et al. , 1993 ; Ioannides et al. , 1995, 2000 ; Tesche , 1996b, 1997 ; Kikuchi et al. , 1997 ; Simos et al. , 1997 ; Nishitani et al. , 1998 ; Baumgartner et al. , 2000 ; Tesche and Karhu , 2000a,b ; Gross et al. , 2002 ; Ikeda et al. , 2002 ; Hanlon et al. , 2003 ; Bish et al. , 2004 ; Kessler et al. , 2006 ; Jerbi et al. , 2007 ; Luo et al. , 2007 ; Martin et al. , 2007 ; Moses et al. , 2007, 2009 ; Cornwell et al. , 2008 ; Kujala et al. , 2008 ; Attal et al. , 2009, 2010 ; Milde et al. , 2009 ; Riggs et al. , 2009 ; Dumas et al. , 2010b, 2011 ; Poch et al. , 2011 ; Quraan et al. , 2011 ). We review some of these below.

Ioannides and colleagues measured amygdalo-hippocam-pal activity during an auditory oddball task (Ioannides et al. , 1995 ) and an identifi cation task of objects and of emotional facial expressions (Ioannides et al. , 2000 ). Source reconstruc-tion was done using the magnetic fi eld topography (MFT) method, which consists of the probabilistic estimates of the primary current density within a predefi ned source space

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Figure 2 MEG sensitivity to cortical and deep structures (DBA model). Displayed are color-coded maps of the normalized average root-mean-squared (RMS) contribution of either neocortical sources (upper part) or deep sources (gray shades, left: hippocampus, right: putamen) using the DBA model with a 151-channel system (CTF Inc.). See ‘ Forward models of subcortical regions ’ for details on the loca-tion and orientation of the DBA model. The head model was com-puted using an overlapping sphere model with BrainStorm software (Tadel et al. , 2011 ). Note that the color bar at the left side is only for neocortical sources. The small color bar at the right side is for all deep sources (gray shades).

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90 Y. Attal et al.

(Ioannides , 1994 ). With this approach, source localization accuracy was estimated to be about 1 – 3 cm for deep sources at 6 cm (Ribary et al. , 1991 ). MFT was also used to study pat-terns of coherent thalamo-cortical 40 Hz oscillations (Ribary et al. , 1991 ). These thalamo-cortical fast oscillations were shown to be altered in patients suffering from Alzheimer ’ s disease (AD) (Ribary , 2005 ). Other early experiments using low-resolution MEG recordings (64-channel whole-head sys-tem) suggested it is the possible to reconstruct activity in the hippocampus and parahippocampal gyrus during an auditory discrimination task using 692 ECD distributed in 30 regions defi ned in individual MRI (Kikuchi et al. , 1997 ).

Using a whole-head MEG 122-channel system, Tesche and colleagues performed a series of studies on the estimation of the activity of several subcortical regions using signal space projection (SSP) (Tesche et al. , 1995, 1996 ; Tesche , 1996a,b, 1997 ; Tesche and Karhu , 2000a,b ). Thalamus activity was reported in response to unilateral median nerve stimulation at the wrist (Tesche , 1996a,b ), hippocampus activity in an auditory evoked response associated with memory encoding (Tesche et al. , 1996 ) and cerebellum activity in somatosensory evoked responses (Tesche , 1997 ). The methods were based on individual MRIs which were used to constrain the source areas (ECD location and orientation) before computing SSP waveforms. More recently, Tesche and Karhu (2000a) also showed hippocampal theta oscillations modeled with ECD sources that were estimated from data obtained during a mem-ory task. ECD source models can also be used to fi t higher frequency oscillations (600 Hz) that may be produced by a cortico-thalamic network (Milde et al. , 2009 ) (for a review on the physiology of 600-Hz-activity, see Curio , 2000 ).

MCE was used to investigate oscillatory activity between the auditory and parietal cortex and thalamus in a binaurally auditory task (Bish et al. , 2004 ) and to detect amygdala activ-ity during a fear conditioning training session when compared to habituation and extinction sessions (Moses et al. , 2007 ). MCE was also applied in simulations of interictal epileptic activity to quantify MEG differentiability of the hippocampal subfi elds, parahippocampal cortex and neocortical temporal sources (Stephen et al. , 2005 ). It was shown that hippocam-pal and parahippocampal sources could be correctly recon-structed in the absence of temporal overlap between regions. However, concurrent activation of dentate gyrus, CA1 or CA3 were not distinguishable because of the temporal overlap and spatial proximity of sources.

Using the DBA model, amygdala activity was recon-structed in a face perception experiment (Dumas et al. , 2011 ) and thalamic activity, especially in the pulvinar, was reported during a resting state task by contrasting eyes open vs. eyes closed conditions (Attal et al. , 2010 ). This result is in agree-ment with subcortical electrophysiological correlates of the alpha rhythm, which were originally found in the pulvinar (Moruzzi and Magoun , 1949 ; Lopes da Silva et al. , 1973 ) and LGN (Lopes da Silva et al. , 1973 ). Using an imaging model restricted to the cortex and the cerebellum in a visuo-motor (VM) task, Jerbi and colleagues used coherence and phase synchronization to highlight long range coupling between the primary motor cortex and multiple brain regions (Jerbi

et al. , 2007 ). They detected a large scale VM network that involved several cortical and subcortical areas, including the cerebello-thalamo-cortical pathway. Similar fi ndings were obtained using dynamic imaging of coherent sources (DICS), a method that combines beamforming and coherence analysis between virtual sensors (sources) (Gross et al. , 2001, 2002 ). Gross and colleagues have initiated a series of studies based on beamformers applied to deep structures, with a particu-lar focus on the amygdalo-hippocampal complex (Luo et al. , 2007 ; Cornwell et al. , 2008 ; Moses et al. , 2009 ; Riggs et al. , 2009 ; Poch et al. , 2011 ; Quraan et al. , 2011 ).

Subcortical activity reconstruction using

dynamic causal modeling

Thus far, we have discussed the possibility of reconstruct-ing deep brain activity in relation to SNR in the scalp data in terms of amplitude of the different brain contributions. Another possibility is to think of subcortical activity from a neurodynamic viewpoint. In other words, because subcortical activity infl uences cortical activity by means of subcortical-cortical connectivity, its changes may be indirectly inferred from M/EEG signals, even though no subcortical activity is directly measured. If one uses biophysical models of M/EEG activity in which synaptic activity is created by artifi cial neu-ronal networks whose activity projects onto the scalp using classical forward head models, then parameters of subcorti-cal activity are latent variables, in contrast to parameters of cortical activity which constitute measured variables. Both types of variables can be inferred from actual M/EEG data, as David et al. (2006) proposed in the framework of DCM; David et al. (2011) recently applied to MEG data from a lan-guage study.

DCM uses interacting neuronal populations to reproduce the activity of brain regions. Using a head model, neuronal current densities are then projected onto the scalp to generate MEG signals. The model can be inverted to fi t the MEG data with biological constraints on reconstructed brain dynamics. Most importantly here, it implies that DCM can estimate the activity of hidden sources from latent variables, i.e., brain regions not actually recorded on the scalp, such as the thala-mus. Using model comparison in DCM (Penny et al. , 2004 ), it is thus theoretically possible to detect the presence of hidden sources (David et al. , 2011 ).

As a proof of concept, we show here a basic simulation (Figure 3 ): two sources were positioned randomly in the brain and the associated forward model was computed using a spherical MEG head model (306 sensors). Their dynamics were created using the neural model of DCM assuming for-ward and backward connections between subsequent sources following two confi gurations: (i) a simple model only com-posed of the two measurable sources in the fi rst visual and motor cortices (V1, M1); and (ii) an augmented model con-taining a hidden source (H) inserted between the two measur-able sources. For each model, scalp data were obtained by integrating the differential equations of the corresponding neuronal models, with parameter values randomly chosen

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Models of subcortical regions for MEG/EEG 91

Cerebral time course

4

420

-2-44

2

0

-2

20

0

0 50 100 150Time (ms)

200 300250

Sou

rce

stre

ngth

(a.u

.)

Orig

inal

Rec

onst

ruct

ed Sou

rce

stre

ngth

(a.u

.)S

ourc

est

reng

th (a

.u.)

50 100 150 200 300M1HiddenV1

250

-2-4

Models

F=-2981

F=-3079

V1

V1 H

M1

M1

V1 H M1

Figure 3 Dynamic modeling of three simulated sources: two cortical and one hidden. The upper row shows the model used to generate a simulated MEG data set. The two lower rows display the estimated time course (left) using either a model with or with no hidden source.

(using the prior mean value plus a random component of maximal value equal to 50 % of the prior variance). Figure 3 shows how the different time series compare to each other. Note how well the activity of the hidden source is estimated (left, second line) when appropriately assumed and how badly the red (high-level) cortical time series is estimated when the hidden source is wrongly assumed to be absent (left, third line). The F value is the negative free energy, an approxima-tion of the log evidence of each model, which is higher for the true model (the one with the hidden source in this case).

M/EEG literature based on this methodology is still scarce but may grow rapidly (to our knowledge, studies on subcor-tical motor loops and on limbic circuits are currently under way). The fi rst MEG study using this approach in cogni-tive neuroscience focused on subcortical-cortical loops dur-ing auditory language processing (David et al. , 2011 ). This study looked at the cortical and subcortical loops supporting the detection of syntactic and prosodic violations in spoken sentences. The authors assumed early activation of subcorti-cal regions after fi rst processing in low-level cortical regions, as suggested by intracerebral EEG recordings obtained in the thalamus of Parkinson ’ s patients with implants (Wahl et al. , 2008 ). DCM of models assuming a hidden subcortical source, possibly the thalamus, reached higher values of exceedance probability than DCM of models with no deep source. The connection strengths within the winning model suggest two separate networks for syntactic and prosodic processes. Moreover, the data suggest that after early auditory process-ing in Heschl ’ s gyrus, anterior superior temporal gyrus and the frontal operculum, error signals are relayed via the thal-amus to cortical regions. These may serve to reactivate the initial representation of the auditory sentence and initiate a repair process. The MEG DCM results go beyond prior analy-ses of M/EEG data on sentence processing in suggesting an involvement of subcortical, possibly thalamic, structures in processing language violations.

Conclusion

Estimating deep brain activity from M/EEG recordings is dif-fi cult but feasible. Two research avenues may improve local-ization accuracy in those regions: (i) improved head models that explicitly take into account important electrical and geo-metrical features of subcortical structures; and (ii) biophysi-cal modeling of subcortical-cortical networks that may allow connectivity patterns to be inferred and latent subcortical activity of the networks to be estimated. Both approaches are worth developing and may bring new tools for future research on deep brain activity in the healthy human brain.

Acknowledgments

The authors thank Denis Schwartz, J é r ô me Yelnik and Dominique Hasboun for fruitful discussions, and Heike Schmidt and Rosie Wallis for their help in preparing the manuscript. This work was sup-ported by ANR (project HM-TC, number ANR-09-EMER-006). OD is funded by Inserm.

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Received August 23, 2011; accepted October 7, 2011

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Models of subcortical regions for MEG/EEG 95

Dr. Yohan Attal graduated with a degree in signal and image processing from the University of Paris-Sud and Ecole Sup é rieur d ’ Electricit é , Orsay, France, in 2006. In 2010, he received a PhD degree from the University of Paris-Sud. His thesis was about acute stroke imaging. He is now a Postdoctoral fellow at the Cognitive Neuroscience and Brain

Imaging Group (Cogimage) from the Brain and Spine Institute (ICM), La Salp ê tri è re Hospital, Paris. He works on electro-magnetic source modeling.

Dr. Burkhard Maess gradu-ated with a degree in phys-ics from the University of Leipzig in 1987. In 1990 he fi nished his PhD on solid state nuclear magnetic resonance. He completed postdoctoral positions at the Academy of the Sciences of the German Democratic Republic in Berlin, the Free University of Berlin and, since 1995, the Max Planck Institute of Cognitive Neuroscience in

Leipzig. Since 2000 he has held a position as senior researcher and head of the MEG Group at the same institute, which, since 2004, has been called the Max Planck Institute for Human Cognitive and Brain Sciences.

Prof. Angela D. Friederici studied psychology and lin-guistics during her PhD which she completed in 1976 at the University of Bonn, Germany. She was then suc-cessively a Postdoc at MIT, Cambridge, MA, USA from 1980 – 1989, a Research Asso-ciate at the Max Planck Insti-tute for Psycholinguistics, The Netherlands from 1989 – 1994, and a Professor of Cognitive

Science at the Free University of Berlin, Germany. Since 1994, she has been a Founding Director of the Max Planck Institute for Human Cognitive and Brain Sciences (formerly MPI of Cognitive Neuroscience) in Leipzig, Germany. She is an Honorary Professor at the Universities of Leipzig (Psychology), Potsdam (Linguistics) and Berlin (Medicine).

Dr. Olivier David gradu-ated in applied physics from Ecole Normale Sup é rieure in 1999 and is an expert in functional neuroimaging and electrophysiology. He was trained in fMRI, MEG/EEG source localization and neuro dynamics (PhD in 2002, CNRS Paris), and MEG/EEG neural modeling (postdoc at

University College London). In 2005, he obtained a senior researcher position to coordinate an EEG/fMRI program at the Inserm U836 Grenoble Institute of Neuroscience, France. Since 2011, he has been the head of the research group ‘ Brain Function and Neuromodulation ’ , Grenoble, that performs clinical and translational research in functional neurosurgery and psychiatry.

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