536790 research-article2014

JOP0010.1177/0269881114536790Journal of PsychopharmacologyMuthukumaraswamy

Review

The use of magnetoencephalography in the study of psychopharmacology (pharmaco-MEG) Journal of Psychopharmacology 2014, Vol. 28(9) 815­–829 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0269881114536790 jop.sagepub.com

Suresh D Muthukumaraswamy

Abstract Magnetoencephalography (MEG) is a neuroimaging technique that allows direct measurement of the magnetic fields generated by synchronised ionic neural currents in the brain with moderately good spatial resolution and high temporal resolution. Because chemical neuromodulation can cause changes in neuronal processing on the millisecond time-scale, the combination of MEG with pharmacological interventions (pharmaco-MEG) is a powerful tool for measuring the effects of experimental modulations of neurotransmission in the living human brain. Importantly, pharmaco-MEG can be used in both healthy humans to understand normal brain function and in patients to understand brain pathologies and drug-treatment effects. In this paper, the physiological and technical basis of pharmaco-MEG is introduced and contrasted with other pharmacological neuroimaging techniques. Ongoing developments in MEG analysis techniques such as source-localisation, functional and effective connectivity analyses, which have allowed for more powerful inferences to be made with recent pharmaco-MEG data, are described. Studies which have utilised pharmaco-MEG across a range of neurotransmitter systems (GABA, glutamate, acetylcholine, dopamine and serotonin) are reviewed.

Keywords Magnetoencephalography, psychopharmacology, electroencephalography, evoked fields, neural oscillations

It would be a factual inaccuracy to suggest that magnetoencephalography (MEG) is a “new” neuroimaging technique, as the first SQUID-based (superconducting quantum interference device) MEG recordings were made by David Cohen in 1972 (Cohen, 1972). From that time, MEG technology gradually developed from single-channel systems and came of age in the 1990s with the advent of whole-head recording systems (Ahonen et al., 1993; Vrba et al., 1993). These MEG systems had an increased number of recording channels and exhibited significant improvements in the extent of head coverage and use of various noise-cancellation technologies. In the 2000s, with the rapid growth of cognitive neuroscience, which was driven largely by developments in functional magnetic resonance imaging (fMRI), there has been a substantial increase in the number of MEG systems. A recent estimate suggests there are now ~160 MEG laboratories worldwide (for an interesting historical perspective see Hari and Salmelin, 2012). The increased availability of MEG has led to growing interest in the use of MEG as a non-invasive measurement tool for human psychopharmacology studies (pharmaco-MEG). In this paper, the physiological and technical basis of MEG is introduced. Practical issues for pharmaco-MEG studies are then discussed and advanced pharmaco-MEG analysis methods detailed. The relative strengths/weaknesses and complementarity of MEG compared to other neuroimaging techniques used in human psychopharmacology is considered. Finally, a review of the pharmaco-MEG literature is provided. For an earlier review of pharmaco-MEG the reader is referred to the review of Kähkönen (2006), whose group pioneered the first systematic pharmaco-MEG experiments.

The physiological and technical basis of MEG The physiological generation of the brain’s magnetic fields The gray matter of the adult human cerebral cortex forms a sheet approximately 250000 mm2 (Heimer, 1994), and its thickness varies across areas from 1.5 to 5.0 mm (average 2.5 mm) (Zilles, 1990). The density of cells within the cerebral cortex, too, varies across cortical areas, from ~20000 (BA11) to ~100000 (BA17) per mm3 (Zilles, 1990). Following the work of Brodmann, the neocortex is traditionally divided into six vertical layers (Figure 1(a)) (Jones and Peters, 1986). Layer one, the molecular layer, lies directly beneath the pia mater and contains mostly glial cells and only a few neurons. Layer two, the external granular layer, contains the cell bodies of small pyramidal cells; while layer three, the external pyramidal layer, is thick with pyramidal cell somata. Layer four, the internal granular layer, contains both pyramidal and non pyramidal cells. Layer five, the internal pyramidal layer, contains large pyramidal cell somata, while layer six, the multiform layer, contains small somata of modified pyramidal cells and projects primarily to the thalamus (Heimer, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK Corresponding author: Suresh D Muthukumaraswamy, Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, CF10 3AT, UK. Email: [email protected]

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Figure 1.  The physiological generation of MEG signals. a) The layers of cerebral cortex as seen with Nissl stains of human visual (left) and motor (centre) cortices and a Golgi stain of infant motor cortex (right). Pyramidal cells in layers three and five are clearly visible (adapted from Ramon y Cajal, 1899). b) Intracellular postsynaptic current flow down an apical dendrite can be considered as an equivalent current dipole (large black arrow). Extracellular volume currents are shown with plain lines and magnetic field lines with dashed lines. Acknowledgement: Image courtesy of Dr Sylvain Baillet, Montreal Neurological Institute. c) An active tangentially-oriented dipole (orange) in the grey matter of the cerebral cortex with its magnetic field located outside the head where it can be detected by an MEG detector. d) Coil configurations and magnetic field patterns in response to a single current dipole (black arrow) produced by the three most commonly used pickup coils in commercial MEG systems. Left to right: magnetometers, firstorder axial gradiometers and first-order planar gradiometers. Images c and d reproduced from Singh K (2009) Magnetoencephalography. In: Senior C, Russell T and Gazzaniga M (eds) Methods in Mind (1st Ed), pp. 291–326 with permission from The MIT Press.

1994). These lamina distinctions are important as the lamina form the underlying anatomical circuitry (Bastos et al., 2012), which generate the MEG/EEG signal and the different cell types make distinct contributions to MEG/EEG. Glutamate is the dominant neurotransmitter of the cerebral cortex. Fast glutamatergic synaptic signaling occurs primarily through activation of post-synaptic, ionotropic α-amino-3hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-D-aspartate (NMDA) receptors leading to the depolarisation of neurons from their resting membrane potential via the influx of Na+ and Ca2+ respectively (Buzsaki et al., 2012). γ-Aminobutyric acid (GABA) is the principal inhibitory neurotransmitter of the cerebral cortex. The inhibitory Cl- currents associated with the GABAA channel opening make a more limited contribution to the external MEG/EEG signal because at resting membrane potential Cl- is close to its equilibrium potential. Neurons such as interneurons and spiny stellate cells have relatively radial dendritic arborisations, which results in a net voltage of approximately 0 when the neuron is depolarised (Lopes da Silva, 2011). This phenomenon was described by the pioneering electrophysiologist Lorente de No (1947) as a “closed-field” configuration. By contrast, cells such as pyramidal cells, whose

dendrites have a predominant direction (longitudinal), form an “open field” and produce net electric potentials (Figure 1(b)), while their transverse current elements tend to cancel (Lopes da Silva, 2011). These inferences, based on empirical observations, have been confirmed more recently by simulating the net current dipoles generated by realistic three-dimensional mathematical models of different neuronal types (Murakami and Okada, 2006). Although action potentials generate relatively large voltages, their short duration (< 2 ms) and typically asynchronous firing patterns mean that they are thought to contribute little to macroscopic MEG/EEG signals (Buzsaki et al., 2012; Lopes da Silva, 2011). While the dendritic shape of GABAergic interneurons means that they do not contribute directly to the MEG/EEG signal in a significant way, these interneurons still play a critical role in shaping the activity of pyramidal neurons. Despite the fact that only ~16% of pyramidal cell synapses (layer two/three) are inhibitory (Markram et al., 2004), inhibitory interneurons are able to control effectively the excitation of pyramidal cells, preventing runaway excitation. The location of inhibitory synapses on cell somata means that the synapses can exert a relatively large influence on neuronal excitability. In addition, inhibitory neurons exhibit relatively high synaptic strength, firing rates and

Muthukumaraswamy resistance to synaptic depression (Markram et al., 2004). Importantly, GABAergic interneurons play a vital role in generating synchronised behaviour among large populations of pyramidal cells, which is necessary for the generation of the MEG/EEG signal. When a GABAergic interneuron causes inhibitory postsynaptic potentials (IPSPs) in the numerous pyramidal cells they synapse upon, the probability of spiking is simultaneously decreased amongst these cells (Gonzalez-Burgos and Lewis, 2008). In the hippocampus, for example, a single interneuron may innervate 1500 pyramidal cells (Sik et al., 1995). Following the termination of the IPSP, the inhibited cells synchronously return to a high probability of firing spikes (Cobb et al., 1995), which results in postsynaptic and local field potentials. In the neocortex, each interneuron can effectively block postsynaptic firing as each makes multiple synapses onto each of their target cells (n=~15) (Markram et al., 2004). The concept of neuronal population synchronisation is very important because large numbers of cells are required to generate a signal strong enough to be detectable to MEG/EEG. The synchronous activation of many dipole sources (primarily pyramidal cells) forms a dipole sheet that, when measured from a distance, can be thought of as a single current dipole (Nunez and Srinivassan, 2006). Based on three-dimensional modeling of the currents generated by realistic neuronal geometry, one estimate suggests approximately 10000–50000 pyramidal cells would need to be synchronously active to create detectable signals (Murakami and Okada, 2006). There is considerable variation in the literature about the area of cortex required to be active to be sufficient to make a measurable MEG or EEG signal. Early experiments using current stimulators suggested that an area of 60 mm2 would be required to produce EEG signals (Cooper et al., 1965). Magnetoencephalographic estimates of the amplitudes of the alpha rhythm suggest that an area of 40 mm2 would be needed (Chapman et al., 1984), while Hamalainen et al. (1993) propose that such an area may be an underestimate given the low current source-density used. Conversely, it has been determined that MEG can detect epileptic spikes from cortical areas as small as 9 mm2 (Barth, 1991), while estimates based on somatosensory evoked activity range from 40 to 400 mm2 (Lu and Williamson, 1991). The neurophysiological principles of MEG and EEG signal generation described so far have been identical but now they start to diverge. MEG predominantly measures the magnetic fields generated by intracellular (impressed) currents, due to their relatively high current density, within the dendrites of pyramidal cells, while EEG measures the electrical potentials associated with volume-conducted extracellular return currents (Figure 1(b)). For a dipole element within a perfect spherical conductor (which the head is not) the external magnetic field is produced only by the dipole, as the magnetic fields produced by volume current are self-cancelling (Cohen and Hosaka, 1976). Although changes in resistance can alter the self-cancellation, these effects are nullified when the resistive boundaries are concentric (Kaufman et al., 1984). Experimental evidence from animal models (Okada et al., 1999) has indeed demonstrated that magnetic-evoked fields are virtually unaffected by the presence of the skull, hence MEG signals and source models are relatively unaffected by skull inhomogeneities (eye sockets) or gross abnormalities, such as those caused by surgical procedures (e.g. craniectomy) (Barth et al., 1986). For these reasons, relatively simple forward models

817 can be successfully used to model MEG fields compared to EEG. The MEG forward problem refers to the problem of how to determine the magnetic fields that result from given primary current source(s), whereas the MEG inverse problem refers to the problem in estimating the location/orientation/amplitude of current source(s) from measured data (Mosher et al., 1999). The accuracy of MEG/EEG inverse solutions depend on the validity of the forward models used. For MEG, a single-sphere model can provide an adequate forward model for current sources, although it is particularly limited for frontal and temporal sources (Hamalainen and Sarvas, 1989). A slightly more complex multiple-sphere model, where a separate sphere is calculated for each of the MEG sensors, has been shown to have similar accuracy to boundary element models (Huang et al., 1999). Typically, spherical models are still used in MEG because boundary element models are significantly more complex to generate, requiring segmentation of anatomical MRI scans into tissue compartments and further rely on literature estimates of the conductivities of brain, skull and scalp (which vary significantly both within and across studies (compare for example Geddes and Baker (1967) and Goncalves et al. (2003)). Another important physical concept is that a radiallyorientated current element produces no magnetic field outside a concentric, homogeneous volume conductor (Sarvas, 1987). Given such a perfect conductor, radially-oriented sources will not be apparent in MEG recordings while tangentially oriented sources will (Figure 1(c)). Both radial and tangential sources should be present in EEG recordings and this may appear to be a limitation of MEG. In an important study using MRI-based cortical surfaces, Hillebrand and Barnes (2002) demonstrated that the amount of neocortex that is radially oriented exists as gyral strips, which are only ~2 mm wide and make up only 5% of the cortical surface. These strips exist at the troughs of sulci and crests of gyri. Regions immediately adjacent to these strips are generally tangentially oriented and hence easily detected by MEG. As discussed earlier, the amount of cortex required to generate an MEG signal is relatively large (compared to the size of these strips) and as such the size of these strips is considerably smaller than the amount of tissue that generates an MEG signal. Thus, Hillebrand and Barnes (2002) concluded that there may be relatively few purely radial sources that are impenetrable to MEG, and that the major limitation of MEG is its insensitivity to deep sources rather than source orientation. The Biot-Savart law states that the magnetic field decays with the square of distance from a current source, and as such a deep source would need to be relatively large in order to be detected by MEG. That said, a number of MEG studies have successfully localised relatively deep MEG sources, including both the amygdala (Cornwell et al., 2008a; Garrido et al., 2012; Moses et al., 2007) and hippocampal/parahippocampal (Cornwell et al., 2008b; Kaplan et al., 2012; Mills et al., 2012; Quraan et al., 2011) areas. Indeed computational models have demonstrated the feasibility of detecting these deep structures with MEG (Attal et al., 2007; Attal and Schwartz, 2013). Finally, in an elegant study providing empirical confirmation of a hippocampal contribution to MEG, Dalal et al. (2013) combined simultaneous depth electrode recordings in the hippocampus with MEG and demonstrated the existence of 0 phase-lag correlated activity between MEG sensor activity and the hippocampus, particularly at the theta frequencies.

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Technical considerations Only a short consideration of the technical basis of MEG will be given here, focusing on those elements critical to the interpretation of pharmaco-MEG data. More detailed accounts regarding the engineering of MEG systems can be found in Vrba and Robinson (2001) and Hamalainen et al. (1993). Commercial MEG systems use dc SQUIDS and flux transformers to detect the magnetic fields of the brain. SQUIDs and their flux transformers rely on superconduction and must be immersed in liquid helium (4° K). The cryogenic dewar, which contains the liquid helium, creates a limiting minimum distance between the pickup coils and the outer surface of the dewar (scalp). For example, for CTF-MEG systems, there is a typical distance of 17.5 mm between the dewar surface and pickup coils. In adults there is considerable variability in scalp to cortex distance. For example, in primary motor cortex a range of 16–26 mm is typically found (Stokes et al., 2005) leading to a total coil to cortex difference of ~35mm. In the future, non-SQUID MEG systems where magnetic field detectors could be placed closer to the scalp could allow for higher signal-to-noise ratio (SNR) recordings (Shah and Wakai, 2013). The other important element that must be addressed is that different MEG systems use different types of pickup coil arrangements and these affect the topography of the magnetic field maps measured for a current dipole source. The three main types of pickup coils used in commercial MEG systems are magnetometers, planar gradiometers and axial gradiometers (Figure 1(d)). A magnetometer consists of a single loop of wire connected to a SQUID, and for a dipole current source produces a field map with a maximum and minimum either side of the dipole. The separation distance of the extrema in the field map indicates the depth of the dipole. When two magnetometer loops of opposite orientation are combined they form a first order gradiometer. Gradiometers detect the change in magnetic field across the two loops. For radial gradiometers, the two loops are oriented parallel to the dipole source and produce similar field patterns to magnetometers with tighter field patterns. For planar gradiometers, the pickup loops are oriented perpendicular to the dipole source and produce field maps with peaks directly above the dipole. The advantage of gradiometers over magnetometers is that gradiometers are less sensitive to distant environmental noise sources, as they measure the difference across the coil. Because environmental sources are relatively distant and large compared to small and local brain fields they exhibit little change across the coil loops while closer brain sources show greater changes. Interpretation of planar gradiometer field maps are perhaps the most intuitive as sources exist as a single maxima below the sensor. Software packages such as FieldTrip (Oostenveld et al., 2011) allow the conversion of data recorded from axial gradiometer systems into a planar configuration, in essence, calculating the second spatial derivative of the recorded field. Vrba and Robinson (2001) provide an excellent introduction and discussion of the different pickup coil configurations. Other commercial MEG systems also employ more sophisticated noise-cancellation techniques relying on hardware-software interactions, for example, the synthesis of higher order gradient formations in the case of CTF-MEG systems (Vrba and Robinson, 2001) and the use of the Signal Space Separation (SSS) technique (Taulu and Simola, 2006) in Elekta Neuromag systems.

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Practical considerations for pharmaco-MEG experiments In our laboratory, we have conducted studies using a range of pharmacological interventions, including both oral (vigabatrin, tiagabine, zolpidem, gaboxadol, alchohol) and intravenous administrations (propofol, psilocybin, ketamine) with a total of >100 interventions. Here, some of the practical experiences of conducting these studies are shared. The necessary location of MEG systems within magnetically shielded rooms (MSR) presents a number of (not insurmountable) challenges for pharmacological studies. As with all MEG studies, the sensitivity of the MEG to environmental noise is such that peripheral experimental equipment must be purpose-purchased/built such that the amount of metal, particularly moving metal parts, is minimised. In terms of pharmacological studies, this means that participant safetymonitoring equipment is required to be located outside of the MSR. There are situations, however, when it is desirable to have medical supervision within the MSR, which can make it difficult for clinicians to use the monitoring equipment. MEG systems generally allow participants to be either seated or supine in the MSR. Increasingly our laboratory makes use of supine recordings, as these recordings typically display decreased head movements, especially when participants are sedated. There is the slight cost issue in the use of supine recordings in that MEG systems will typically display higher helium boil off rates when horizontally oriented. With the recent advent of helium recyclers that can be retrofitted to most MEGs (at significant expense) this cost will be decreased. However, supine recordings do present several problems. Firstly, the clarity of visual displays is typically inferior in supine compared to seated positioning. Projection systems must be reflected off more mirrors in supine positioning, which can create slight geometric warping in the images displayed to participants. With seated recordings the opportunity exists for the direct viewing of high-quality visual displays. Secondly, eye-tracking is more difficult in supine recordings, though it is not without difficulty in seated MEG recordings because cameras cannot be directly mounted to the head. Increasingly, we are acknowledging the importance of recording extra physiological signals with the MEG. Recording of ECG is useful for monitoring changes in heart rate and potentially can be used for eliminating ECG artefacts in the MEG recordings. Similarly, electrooculography (EOG) can be used as a rough measure of eye movement and can aid in the removal of ocular artifacts from MEG data. Measurements of simultaneous EEG/ MEG have proven to be very useful in studies investigating pharmacological modulation of the mismatch negativity (reviewed later), where it is well known that later components from the frontal cortex appear to be absent (Deouell, 2007; Rinne et al., 2000). Finally, inclusion of psychometric and other peripheral physiological measures can be very useful in studies where null results are obtained, as they can be used to confirm penetration of the central nervous system by the drug in question.

Analysis of pharmaco-MEG data Many of the mathematical operations used in pharmaco-MEG data analysis are identical to those used in standard EEG/MEG research; this includes operations such as artifact rejection and/or correction, baseline correction, filtering and signal averaging, for

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Figure 2.  Upper: Group-level source localisations centered on the precentral (left) and postcentral gyri (right) showing the increase in beta (15–25 Hz) power following 5 m of diazepam. Lower: Time-frequency analysis of reconstructed virtual sensor activity from the primary motor cortex. See text for details. Adapted from Hall SD, Barnes GR, Furlong PL, et al. (2010a) Neuronal network phar­macodynamics of GABAergic modulation in the human cortex determined using pharmaco-magnetoencephalography. Hum Brain Mapp 31: 581–594 with permission from John Wiley & Sons, Inc.

which readers are referred to several sources (Gross et al., 2013; Hamdy, 2005; Schomer and Lopes da Silva, 2012). Here, several pharmaco-MEG studies that make use of more advanced analytical techniques that potentially maximise the rich information in MEG data are considered. As discussed above, one advantage of MEG over EEG is the reduced complexity involved in the localisation of sources. A vast array of techniques exists for MEG source-localisation, which often differ from each other with regard to the assumptions made in order to solve the inverse problem. Two of the more commonly used classes of techniques are beamformer-based solutions and minimum-norm current estimates. In minimumnorm estimates, a large number (thousands) of potential generator-dipoles locations and their lead fields are defined; usually based on the participant’s gray matter cortical sheet. The primary current distribution with the smallest norm is selected from all potential current distributions that could generate the measured magnetic field. In L2-norm solutions the integral of the square of the primary current density is minimised, whereas in L1 minimum norm solutions the integral of the absolute value of the primary source current is minimised (Hamalainen and Ilmoniemi, 1994). By contrast, beamformer-based solutions (Gross et al., 2001; Robinson and Vrba, 1999; Van Veen et al., 1997) do not attempt to estimate the amplitude of all modeled source locations simultaneously. Rather, at each voxel in an arbitrarily defined three-dimensional source space, spatial filters are created that estimate source activity when multiplied with the measured data. These spatial filters seek to preserve activity at the source location while minimising interference from other locations. Some of the most common implementations of beamformers include the linearly constrained minimum variance (LCMV) beamformer (Van Veen et al., 1997), synthetic aperture magnetometry (SAM) (Robinson and Vrba, 1999) and dynamic imaging of coherent courses (DICS) (Gross et al., 2001). The relative strengths and

weaknesses of the two classes of source solutions for pharmacoMEG data have yet to be evaluated. An elegant example of a beamformer-based approach to pharmaco-MEG data was provided by Hall et al. (2010a) (see Figure 2). Here, participants were given a 5 mg oral dose of diazepam, followed by a 60-minute resting eyes-open MEG acquisition. The SAM beamformer was used to localise changes in resting spectral power. The activity at brain regions, which showed changes in source power, was reconstructed and time-frequency analyses performed. As such, the authors were able to spatiotemporally map the pharmacodynamics of resting power changes following drug administration. A similar approach can also be taken to event-related designs; this is exemplified in the work of Bauer et al. (2012) (see Figure 3). In this task, participants were cued to attend either to the left or right visual field and asked to perform an orientation-judgement task on a grating patch in the attended part of the visual field. This task was performed following intravenous administration of either the cholinesterase inhibitor physostigmine (0.01 mg/kg/hr, i.v.) or a placebo. In Figure 3 it can be seen that the authors localised activity in the gamma-frequency band (50–70 Hz), in two areas, the visual cortex (panel A) and the motor cortex (panel E). Reconstructing the time-course of activity in the source domain clearly shows that while the occipital gamma source increased in power during the task, there were no differences between drug and placebo. Conversely, the frontal source showed increased gamma power during the task but significantly more so under drug compared to placebo. Hence, using the beamformer the authors spatiotemporally mapped differential eventrelated gamma effects caused by increased cholinergic activity. Several recent methodological papers have shown that the resting-state networks frequently reported in fMRI connectivity studies (Smith et al., 2009), such as the dorsal attention network and default mode network, can be seen in MEG data. In these approaches source-level MEG data are generated and the

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Figure 3.  Source localisation of the gamma-band (50-70 Hz) showing activity in the visual and frontal cortices with corresponding temporal dynamics. See text for details. Reproduced from Bauer M, Kluge C, Bach D, et al. (2012) Cholinergic enhancement of visual attention and neural oscillations in the human brain. Curr Biol 22: 397–402 with permission from Elsevier.

temporal correlations between the band-limited power of the sources are examined (Brookes et al., 2011; de Pasquale et al., 2010). Recently, we applied (Muthukumaraswamy et al., 2013a) one of these techniques (Brookes et al., 2011) to resting-state MEG data following intravenous administration (2 mg) of the classical hallucinogen psilocybin. In total, ten networks were identified in the data, seven of which showed a significant reduction in activity following psilocybin Figure 4). One advantage of using MEG to perform these resting-state analyses, traditionally the domain of fMRI, is that these data have no danger of indirect contamination from vascular factors. The final approach covered here, dynamic causal modeling (DCM) for MEG, is one with enormous potential for increasing the inferences that can be made from pharmaco-MEG data. At the heart of DCM for MEG is the construction of a neuronal mass model (Wilson and Cowan, 1972) for how cortical regions or sub-populations of cells (considered as masses rather than individual elements) interact. A forward model is added to the neuronal model that specifies how synaptic activity is transformed into a measured MEG signal (Friston et al., 2003; Kiebel et al., 2008). Given measured data, for example a source-level MEG spectrum, the generative model can be inverted (fitted to the data) and various parameters of the neuronal model, such as the efficacy of connections between cell-types, estimated. Moran et al. (2011) demonstrated how DCM could be used to estimate within-region parameters on task-based spectral data to make non-invasive “synaptic assays” of specific neural parameters,

such as the effective connectivity between cell types. In their experiment, participants performed a working-memory task after placebo or levodopa administration (100 mg oral). Frontal cortex theta was found to be generally increased following levodopa and a significant drug by working-memory interaction effect was found to be localised in the right superior frontal cortex (Figure 5(a)). The investigators developed a neural-mass model that consisted of three main cell types: pyramidal cells, GABAergic inhibitory interneurons and stellate cells. Synapse types included GABAA receptors, AMPA receptors (fast excitation) and NMDA receptors (slower excitation) (Figure5(b)). Inverting the source-level MEG spectra allowed various parameters in the model to be estimated. Interestingly, the authors found two parameters that correlated with the improvements in working memory during the task. The authors were thus able to conclude that the neuromodulatory effects of dopamine via enhancing NMDA receptor nonlinearity and decreasing fast excitatory pyramidal to stellate connectivity was predictive of improved memory performance. The full power and range of potential uses of DCM is yet to be explored with pharmaco-MEG data. DCM for MEG is open to the criticism that the inferences from DCM are only as good as the models used and these may be too simplistic given the vast complexity of the underlying cell populations. However, it may be that such complexity is not needed to model the behaviour of the MEG signal. PharmacoMEG could play a useful role in validating the neuronal models used in DCM (Bastos et al., 2012).

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Figure 4.  A reduction of MEG resting-state network activity following intravenous administration of the classic hallucinogen psilocybin. See text for details. From Muthukumaraswamy et al. (2013a).

MEG in comparison to other neuroimaging methods for psychopharmacology MEG has advantages and disadvantages over other neuroimaging techniques when used in a pharmacological context and these are considered here.

EEG As described above the generator mechanisms of MEG and EEG are very similar. One of the principal advantages of MEG is that relatively simple forward models can be used allowing improved localisation of sources over EEG. Another advantage of MEG is that physiological artifacts from the head, such as blinks and muscle movements, are more focal in MEG than in EEG where they are volume conducted more diffusely (Carl et al., 2012; Claus et al., 2012). In fact, a series of studies have shown that even “clean” EEG data in the higher frequency range (gamma-band) can be heavily contaminated with muscle artifacts (Fitzgibbon et al., 2012; Whitham et al., 2007, 2008). The net result of this is that for stimulus-induced gamma-band activity, MEG has a superior signal-to-noise

ratio to EEG (Muthukumaraswamy and Singh, 2013). In practical terms, pharmaco-MEG can be advantageous over pharmacoEEG in that participants are more comfortable for long recording days and do not have to have electrodes applied for long periods of time. Conversely, pharmaco-MEG studies of sleep would be difficult to conduct due to head movements, and indeed none have been published, whereas sleep studies are common in the pharmaco-EEG literature. Another disadvantage of MEG is that at present, the technology is extremely expensive and must be fixed with magnetically shielded rooms which are completely immovable. EEG systems can be set up virtually anywhere, and for example, can be easily located in registered clinical trials facilities. For these reasons, pharmaco-MEG has yet to play a role in experimental medicine investigations.

fMRI It is often said that fMRI has high spatial resolution but poor temporal resolution, whereas MEG (or EEG) has relatively poor spatial resolution and good temporal resolution, and that as such the two techniques are complementary. While this is broadly true, this simple argument glosses over the fact that the two techniques measure very different weightings of neural activity (see Singh, 2012 for a discussion). While the BOLD fMRI signal probably

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Figure 5.  Demonstration of the use of DCM on pharmaco-MEG data. See text for details. Adapted from Moran RJ, Symmonds M, Stephan KE, et al. (2011) An in vivo assay of synaptic function mediating human cognition. Curr Biol 21: 1320–1325 with permission from Elsevier.

reflects the integral of a broad range of different neural activities, the MEG signal represents a more limited range of activity (synchronised postsynaptic potentials). In a pharmacological context, a major advantage of MEG (or EEG) over blood-flow based methods is not only that the technique is quantitative (fMRI measures are relative), but the BOLD signal can be indirectly modulated in drug studies by the interaction of drugs with the cerebral vasculature (Iannetti and Wise, 2007). It can be difficult in BOLD fMRI to disentangle regional vascular changes with genuine changes in neuronal activity. That said, BOLD fMRI does offer exquisite spatial resolution and the ability to record activity from deep structures, such as the brainstem, that are inaccessible to MEG.

Positron emission tomography (PET) The radiolabelling of selective tracer ligands and their threedimensional quantification as a function of time with PET can be used to study glucose metabolism, blood-brain barrier transport,

neurotransmitter release, receptor density quantification and for tracking the pharmacodynamic properties of drugs, including receptor occupancy, EC50 and time-course. Some neurotransmitter systems can be well characterised, for example, the serotonin (5-HT) system where ligands exists for 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 receptors, and for the 5-HT transporter (SERT) (Paterson et al., 2013). This broad range of applications as well as pharmacological specificity allows neurotransmitter systems to be deconstructed in great detail and probably makes PET the most useful of all the human pharmacological neuroimaging techniques. The disadvantages of PET include low spatial and temporal resolution, massive expense, radiochemical production, purity analysis, pre-clinical validation, radiodosimetry, and tolerance of i.v. injection of a radioactive compound. The information that MEG provides with regard to the functional activity of cortical neuronal networks is potentially highly complementary to PET. No studies exist yet which have combined PET and MEG in the same study population, let alone in a study with a pharmacological challenge.

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Magnetic resonance spectroscopy (MRS)

GABA

MRS allows the direct detection of endogenous metabolites in the human body non-invasively in vivo. Of the various metabolites that can be imaged, the most potentially interesting from a psychopharmacological perspective are GABA, glutamate and glutamine. Although several studies have combined MEG with MRS (Gaetz et al., 2011; Muthukumaraswamy et al., 2009), the interpretation of the bulk metabolite concentrations MRS measures is difficult because it does not distinguish the active neurotransmitter pool from the rest. For example, while GABA concentrations are usually measured in the millimolar range the concentration of synaptic GABA is in the nanomolar range (Farrant and Nusser, 2005). Thus, the bulk of the signal measured is not synaptic in nature but is rather stored in neurons and glia. Indeed, one MRS study failed to find an increase in bulk GABA concentration with GAT-1 blocker tiagabine (Myers et al., 2013), a drug that increases synaptic GABA concentration (Fink-Jensen et al., 1992) when measured with in vivo microdialysis. On the other hand vigabatrin, which blocks the catabolism of GABA to glutamate by GABA-transaminase, causes increased GABA concentrations when measured with MRS (Petroff et al., 1999) and probably indicates increased (intracellular) pooling of GABA. Several studies have indicated increased MRS glutamate levels following administration of the NMDA antagonist ketamine (Rowland et al., 2005; Stone et al., 2012), but combined MEG/ MRS studies of ketamine have not been performed.

Following a long-history of pharmaco-EEG investigations into GABA-enhancing agents (e.g. Saletu et al., 1987), the effects of a (30 μg/kg, i.v.) administration of the benzodiazepine lorazepam (a non-selective positive allosteric modulator) of GABA on the MEG were described (Fingelkurts et al., 2004). Analyses in the sensor-space revealed a decrease in the percentage of time with alpha activity, but an increase in delta and theta activity, and no changes in beta rhythm. Subsequently, the effects of 80 μg/kg (oral) diazepam (a non-selective positive allosteric modulator) on central beta oscillations were described (Jensen et al., 2005). Diazepam was found to increase the power and decrease the frequency of central beta oscillations with current source estimation techniques suggesting the effect was maximal in sensorimotor cortex. Exploring potential neuronal mechanisms that could generate these effects, the authors simulated a neuronal model containing excitatory and inhibitor interneurons. In the model, increasing GABAergic conductance was found to generate a similar slowing and broadening of the beta rhythm peak. Further, exploring the ability of MEG to localise resting spectral changes in the brain, Hall et al. (2010a) examined the spectral pharmacodynamics following a 5 mg oral dose of diazepam (Figure 1). The pharmacodynamic profile showed localised source power increases in occipital and frontal gamma, central (sensorimotor) beta and occipitotemporal alpha rhythms and source power decreases in frontal theta rhythms. The time-course of changes was consistent with the expected uptake of drug. This study is an important example of how MEG can be used to spatially and temporally characterise pharmacodynamics in the brain. In a subsequent translational research study, Ronnqvist et al. (2013) examined the spectral responses in sensorimotor cortices to zolpidem (0.05 mg/kg, oral), with both MEG recordings made in humans and local field potentials recorded from rat slices take from the sensorimotor cortex in vitro. The authors noted an increase in beta rhythm power following zolpidem in human MEG recordings that was present in both layers three and five in vitro. It was further demonstrated that MEG signals from sensorimotor cortex represented a weighted combination of potentials from layers three and five of the cortex; this is an important finding for translating data from animal models to human MEG studies. In a case study of a single participant (Hall et al., 2010b), who had suffered from a unilateral left temporal lobe lesion that was characterised by pathologically increased theta and beta rhythms in the left hemisphere, a sub-sedative dose of the GABAA alpha 1 subtype selective agent zolpidem (5 mg, oral) was found to reduce the amplitude of these pathological brain oscillations. The reduction in the amplitude of these oscillations was paralleled by improvements in cognitive function. The non-selective agent zopiclone (3.5 mg, oral) (alpha 1, 2, 3 and 5) had no effects. In a case report demonstrating the MEG effects of an acute tiagabine overdose (15 mg, oral) in three healthy women (Hamandi et al., 2014), rhythmic 2–3 Hz activity resembling toxic encephalopathy was observed with no intermixed spikes or sharp waves (Azar et al., 2013). This was possibly due to spillover of excess GABA from the synapse and a subsequent increase in tonic GABA currents (Farrant and Nusser, 2005). Induced responses in primary motor and visual cortices have been examined using a variety of GABA receptor allosteric

Limitations of pharmaco-MEG In comparison with invasive approaches in animal models, where electrical activity can be considered at the level of ion channels through to local field potentials, pharmaco-MEG in humans is coarse in terms of both spatial resolution and signalto-noise ratio. Further, invasive approaches allow the recording of subcortical structures, many of which are inaccessible to MEG. There may be cases where differential pharmaco-MEG activity is driven by pharmacological modulation of subcortical areas, such as the thalamus, subthalamic or even brainstem nuclei rather than via direct cortical modulation. In animal models these scenarios can be teased apart by local application of drugs. Indeed, because agents must be delivered systemically in pharmaco-MEG, there is also the risk that the effects of peripheral nervous system modulation feed-forward into the central nervous system and generate observable differences in MEG activity. Only careful experimental design and cautious interpretation of data can mitigate these factors. However, because MEG allows recording of mean field potentials in healthy and clinical human populations, pharmaco-MEG provides a potentially powerful bridge for the translation of animal models, where analogous field potentials can be recorded (for example, Fries et al. 2008).

A review of pharmaco-MEG studies Here a systematic review of reported pharmaco-MEG studies is given. Unlike the review of Kähkönen (2006), who divided his review by experimental paradigm, literature is divided by (the primary) neurotransmitter system studied.

824 modulators. In the motor cortex, movement-related beta desynchrony was found to be facilitated by diazepam (5 mg, oral) (Hall et al., 2011); however, no changes in post-movement beta rebound or movement related gamma synchronisation were seen. Similar results were also reported using the GABAreuptake inhibitor tiagabine (15 mg, oral) (Muthukumaraswamy et al., 2013b), although in that study a reduction in postmovement beta rebound was also reported. In the primary visual cortex tiagabine (Muthukumaraswamy et al., 2013c) was reported to have no effects on induced gamma oscillations, but large reductions in visual evoked responses were noted. By contrast, the (non-selective) positive allosteric modulator of the GABAA receptor propofol (sedated to OAA/S level 4 (Thomson et al., 2009)) was found to not only reduce evoked responses, but to also increase induced gamma activity and alpha desynchronisation (Saxena et al., 2013) (see the section on alcohol below for further discussion).

Glutamate Almost all the MEG studies examining glutamatergic systems have focused on the NMDA receptor subtype, mostly using the antagonists ketamine and memantine. Kreitschmann-Andermahr et al. (2001) examined the effects of a sub-anaesthetic (0.3 mg/ kg, i.v.) dose of ketamine on the mismatch negativity and found decreases in the amplitude and increases in latency of the response to mismatch stimuli, but no alteration for standard tones. Conversely, however, a 30 mg oral dose of memantine did not affect the magnetic mismatch negativity (Korostenskaja et al., 2007). Finally, in the auditory system it has been found that click stimuli are able to inhibit subsequent tone-bursts, but steady-state infusion of ketamine (0.27 mg/kg, i.v.) diminishes this inhibition (Boeinga, 2007). These studies suggest that ketamine modulates pre-attentive auditory processing, but not the earliest auditory responses. In a set of studies the effects of ketamine infusion on patients with major depression has been examined, with ketamine being used for its rapid anti-depressant properties (Berman et al., 2000). Examining the ability of MEG to predict the anti-depressant effects of ketamine (Salvadore et al., 2010), it was found during an N-Back working memory task that low-levels of pregenual anterior cingulate cortex (ACC) activity as well as pregenual ACC to amygdala coherence was correlated with clinical improvement after a subsequent dose (0.5 mg/kg, i.v.) of ketamine. Similarly, pretreatment rostral ACC activity in response to fearful faces was found to be predictive of the clinical response to ketamine (Salvadore et al., 2009). These important studies demonstrate the usefulness of MEG in identifying potential biomarkers of effective treatment response in major depressive disorder, a usefulness that has yet to be extended into other psychiatric or neurological disorders. In order to examine potential mechanisms by which ketamine exerts its anti-depressant action, the effects of ketamine infusion (0.5 mg/kg, i.v.) on subsequent (6 hours post) MEG activity in a major depressive disorder were examined (Cornwell et al., 2012). It was found that those patients who exhibited improvements in depressive symptoms also had enhanced stimulus-evoked gamma band responses (~40 Hz) to air puff somatosensory stimuli. No changes in baseline gamma-band activity in the somatosensory cortex were identified. This relatively long-term change suggests

Journal of Psychopharmacology 28(9) that increased somatosensory cortical excitability (and perhaps cortical excitability in general) measured with MEG might be a cortical maker of anti-depressant action. Based on animal models, the authors suggest that this long-term change is mediated by a post-ketamine increase in AMPA neurotransmission.

Acetylcholine Osipova et al. (2003) compared the effects of the muscarinic receptor antagonist scopolamine (0.3 mg, i.v.) with glycopyrrolate (0.2 mg, i.v.), a muscarinic receptor antagonist which does not cross the blood brain barrier, on the MEG power spectrum, in a group of healthy elderly participants. They found an increase in theta activity with scopolamine and a reduction of eyes closed/ open spectral ratio in the alpha band, suggesting a reduction of the alpha desynchronisation caused by eye opening. They also noted a significant reduction of interhemispheric coherence in the theta band. Bauer et al. (2012) (Figure 2) investigated the effects of the acetlycholinesterase inhibitor physostigmine (0.01 mg/kg/hr, i.v.) on stimulus-induced activity during a visual attention task. Physostigmine enhanced the effects of spatial attention on alpha/ beta activity in the visual cortex. No effects on gamma-band activity in the visual cortex were seen; however, there was an enhancement of gamma activity in the frontal cortex. Scopolamine (0.3 mg, i.v.) has also been shown to enhance the magnetic auditory steady-state 40-Hz response (Ahveninen et al., 1999). Several studies have examined the effects of cholinergic drugs on evoked fields in somatosensory and auditory cortices. Scopolamine (0.3 mg, i.v.) was found to reduce the P35m and P60m components of the somatosensory-evoked field (Huttunen et al., 2001), while nicotine (4 mg, buccal) was found to enhance change-related responses in secondary somatosensory cortex, but not primary somatosensory cortex (Kodaira et al., 2013). This is similar to the effect of nicotine on change-related auditory responses, where the early 50 ms component is unaltered, while the later 120 ms component was enhanced by nicotine (4 mg, buccal) (Otsuru et al., 2012). Participants with relatively small baseline change-related responses had larger percentage changes with nicotine. Finally, scopolamine (0.3 mg, i.v) was found to reduce mismatch negativity responses to frequency mismatches and delayed N100m latency (Pekkonen et al., 2001); however, a separate study (0.3 mg, i.v) found enhancement of middle latency (50 ms) auditory responses (Jaaskelainen et al., 1999).

Dopamine In a study using levodopa (100 mg, oral) it was found that increasing dopamine levels increased prefrontal theta power, an effect which was particularly strong during a working memory task (Moran et al., 2011). Using DCM for steady-state responses, three types of synapses, GABA, NMDA and AMPA, were modeled (Figure 4). It was found that the observed changes in spectral power could be explained by an increase in NMDA non-linearity and a decrease in non-local glutamatergic input. Further, the modulation of NMDA nonlinearity strongly positively correlated with the improvement on the working memory task seen with levodopa. This study demonstrates the enormous potential for MEG data to be combined with DCM to infer the effect of (hidden) cellular-level events, not only on MEG spectra, but on distinct psychological processes that can accompany these spectra,

825

Muthukumaraswamy in this case, working memory. Levodopa (150 mg, oral with 37.5 mg Benserazide) has also been shown to impair accuracy in old/ new recognition memory tasks and slow MEG signals in occipitotemporal sensors which discriminate new from previously seen items (Apitz and Bunzeck, 2013). This same dose of levodopa (150 mg, oral with 37.5 mg Benserazide) accelerated medial temporal lobe fields related to novel stimuli, while the acetylcholinesterase inhibitor galantamine (8 mg, oral) had no effect (Eckart and Bunzeck, 2012). Several evoked field studies have been performed with the typical antipsychotic haloperidol (D2, D3, D4 inverse agonist). These studies showed that 2 mg of oral haloperidol did not alter early auditory evoked fields (Kähkönen et al., 2001; Pekkonen et al., 2002) but decreased the latency of frequency related mismatch negativities (MMNs) (Pekkonen et al., 2002). This dose also decreased the amplitude and latency of re-orienting responses during an auditory mismatch task, suggesting that dopamine disrupts re-orienting after distraction from a task (Kähkönen et al., 2002a). The same dose of haloperidol had no effect on the early responses of the somatosenory cortex to electrical stimulation, but the later P60m was slightly reduced (Huttunen et al., 2003). Given the extensive evidence of alterations of MMN responses in patients with schizophrenia (Garrido et al., 2009; Todd et al., 2013), systematic pharmaco-MEG investigations of the MMN (see Kähkönen (2006)) have provided valuable insight into the neurobiology underlying MMN alterations in this disorder. Several other studies have examined the effects of levodopa and other dopaminomimetics on MEG activity in patients with Parkinson’s disease. Stoffers et al. (2007) examined the effect of levodopa challenge (patient-specific doses) in 37 levodopatreated patients. They reported decreased relative power in right frontal theta, left occipital beta and left temporal gamma, and an increase in right parietal gamma power. All changes in power were relatively small, probably due to the extensive medication histories of the patients involved. Subsequent analysis of a subset of the same patient group showed that the dopaminomimetic challenge increased resting-state cortico-cortical functional connectivity (Stoffers et al., 2008) (synchronisation likelihood (Stam and van Dijk, 2002)) in the 4–30 Hz range. Simultaneous intracranial recordings from the subthalamus and MEG in patients with Parkinson’s disease (Litvak et al., 2011) have shown that dopaminergic medication (patient-dependent, ≥ 200mg, oral) increases the coherence between the prefrontal cortex and subthalamus. Further, during movement tasks in these patients (Litvak et al., 2012), levodopa enhanced power in the 60–90 Hz in both the subthalamic nucleus and primary motor cortex. This enhancement effect was correlated with an observed decrease in bradykinesia. The finding that subthalamic nucleus activity drives motor gamma activity provides an important cautionary example of how cortical (MEG) activity can be driven by subcortical activity.

Serotonin In the first study combining MEG with a classical hallucinogen, Muthukumaraswamy et al. (2013a) examined the effects of the mixed serotonergic agonist (5-HT2A/1A/2C) psilocybin (2 mg) delivered intravenously on spontaneous and induced cortical MEG activity (Figure 3). Psilocybin was found to decrease cortical oscillatory power in a broad frequency range (1–100

Hz), with the largest effects being found in cortical association areas. Concomitantly, the decrease in the activity of whole brain networks (Brookes et al., 2011), such as the dorsal attention network and default mode network, were found to be reduced. DCM of the spectral activity reconstructed from the posterior cingulate cortex suggested that the desynchronisation observed in this area could be attributed to increased excitability of deep (layer five) pyramidal cells, a result consistent with the known distribution of 5HT2A receptors (Aghajanian and Marek, 1997). Despite the observed large changes in resting spectra, induced gamma oscillations in the primary visual and motor cortices were unaffected by psilocybin (Muthukumaraswamy et al., 2013a). Unfortunately, there have been few other MEG studies examining the broad range of serotonergic compounds available for human use. Several studies have examined the effects of acute tryptophan depletion, whereby dietary restriction of tryptophan is used to decrease the synthesis of serotonin in the brain, on evoked magnetic fields. Acute tryptophan depletion did not alter somatosensory evoked fields (Kähkönen et al., 2003), but it did cause small alterations in early auditory evoked magnetic fields (Kähkönen et al., 2002b, 2002c).

Alcohol The effects of alcohol (0.8 g/kg, oral) on spontaneous MEG rhythms have been examined (Nikulin et al., 2005). In eyesclosed conditions, alcohol bilaterally increased the relative power of alpha rhythms and reduced the relative power of beta activity. Interestingly, simultaneous EEG recordings showed no differences, suggesting that MEG had superior sensitivity than EEG. Similarly, Rosen et al. (2014) showed that alcohol (0.6 g/kg men/0.55 g/kg women, oral) strongly increased alpha power in eyes-closed conditions with minimum norm source estimates, identifying sources in the medial occipitoparietal area. Small increases in theta and beta were also seen with alcohol localised to the anterior cingulate cortex. In an event-related study of sensory systems (Kähkönen et al., 2005), alcohol (0.8 g/kg, oral) reduced the amplitude of N1m and MMNm amplitudes bilaterally but did not alter their latency. Conversely, in the visual and motor systems (Campbell et al., 2014), alcohol (0.8 g/kg) enhanced the amplitude of task-dependent gamma-band activity, lowering the frequency in the visual system. The increase in gamma-band activity in the visual system was analogous to that seen with propofol (Saxena et al., 2013); see section above on GABA) and when these results are considered together suggest stimulus-induced gamma-band activity in the visual system is sensitive to the activity of GABAergic interneurons. Given that alcohol impairs cognitive control, Kovacevic et al. (2012) examined MEG responses during a Stroop task. Although reaction times were similar, participants made significantly more errors and corrective responses to incongruent trials following a moderate dose of alcohol (0.6 g/ kg men/0.55 g/kg women, oral). Event-related theta power was localised to frontal, parietal and anterior cingulate cortex (ACC), with theta power in the ACC reduced with alcohol. This fits with the theoretical accounts, which stress the importance of ACC in cognitive control. With a similar dosing paradigm (Marinkovic et al., 2012) during a lexical deci­sion task, alcohol reduced reaction

826 times and marginally reduced response accuracy. Alcohol was found to reduce theta responses to words over non-words, indicating an interference with semantic retrieval. Further, theta power in the ACC and prefrontal cortices, which are usually sensitive to decision-making, was modulated by alcohol. In a modified lexical-decision task, this time examining the time course of changes, Marinkovic et al. (2014) showed that alcohol (0.6 g/kg men/0.55 g/kg women, oral) decreases the amplitude of early occipitotemporal responses to all conditions but enhanced left anteroventral prefrontal cortex activity (M400) only to real words, which require the processing of lexical-semantic access. Together, these three studies elegantly demonstrate the ability of MEG to localise drug-related brain changes in relatively complex cognitive tasks.

Others The stimulant methylphenidate (40 mg, oral), a dopaminenoradrenaline reuptake inhibitor used in the treatment of ADHD (e.g. Ritalin®), was found to have no effect on MMNm amplitudes (Korostenskaja et al., 2008). A series of MEG studies have examined the effects of dextroamphetamine treatment on adult ADHD patients. While methylphenidate is purely an uptake inhibitor, dextroamphetamine has additional presynaptic actions, releasing dopamine and noradrenaline from presynaptic neurons (Arnold, 2000). In a first study (Franzen and Wilson, 2012), gamma-band activity was examined during an auditory oddball task before and after participants took their daily dose of dextroamphetamine. Dextroamphetamine was found to significantly decrease medial prefrontal event-related desynchronisation in the medial prefrontal cortex and in the left superior parietal region and to decrease event-related synchronisation in right superior parietal, left inferior frontal, parietal and occipital areas. However, no placebo session or control was included. In a subsequent study (Wilson et al., 2013), resting state MEG was compared between adults and patients before and after dextroamphetamine administration; the results showed that there was a significant increase in prefrontal alpha activity only in adults with ADHD, and a significant increase in low-frequency phase-locking between nodes of the default-mode network (Franzen et al., 2013). In a similar design using an auditory attention task (Heinrichs-Graham et al., 2014), it was found that unmedicated adults with ADHD exhibited higher phase coherence in beta (14–30 Hz) and gamma (30–56 Hz) frequency bands and that treatment with dextroamphetamine reduced these differences. These studies therefore present MEG evidence of aberrant connectivity in ADHD and suggest that current treatments might work by normalising hyper-activity. Finally, target-controlled infusion (1.0 ng/mL, effect-site concentration) of the mu-opioid receptor agonist remifentanil had no effect on the MMNm or the novel P3a(m) in 20 healthy participants (Quaedflieg et al., 2013).

Conclusions: The future of pharmacoMEG Following the early work of Kähkönen, several research groups are now actively engaged in pharmaco-MEG research. As

Journal of Psychopharmacology 28(9) discussed throughout this review, future pharmaco-MEG research could be enhanced by the following: 1. An increased emphasis on drugs with well-specified mechanisms of actions and comparison of drugs with similar mechanisms of action to test the specificity of MEG signal alterations to pharmacological manipulation. 2. Examination of dose-dependent and pharmacodynamic effects on the MEG. 3. Increased use of pharmaco-MEG in patient populations, for example, as biomarkers of treatment efficacy. 4. Validation of analytical techniques used on pharmacoMEG data. 5. Multi-modal neuroimaging studies with identical pharmacological challenges, especially in similar (identical) volunteer cohorts. 6. The development of MEG in collaboration with industry as a tool that can be used in early-phase drug development. Given the enormous range of possible pharmacological interventions, experimental protocols and data analysis techniques that MEG allows there is still an enormous amount of potential research opportunities. As such, the future of pharmaco-MEG in psychopharmacological research is promising.

Acknowledgements The author would like to thank Dr Jim Myers and Dr Dina Dosmukhambetova for their comments on the manuscript, and the many collaborators who have discussed some of the ideas that have been developed here.

Conflict of interest The author declares that there is no conflict of interest.

Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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