A pilot study of the effect of short-term escitalopram treatment on brain metabolites and gamma-oscillations in healthy subjects

Understanding the neural substrates underlying pharmacological action on different neurotransmitter systems is a key facet of neuroscience research and critical in the development of new treatments in psychiatry (Matthews et al., 2006). The initial efforts to incorporate combined neuroimaging techniques, including magnetoencephalography (MEG) and proton magnetic resonance spectroscopy (MRS), into the study of brain functioning indicate a high potential of such an approach in advancing the research on pharmacotherapy of psychiatric disorders, including depression and anxiety disorders. For example, the possible sensitivity of MEG measures to γ-aminobutyric acid (GABA) mediated inhibition suggests that the measurement of induced gamma-oscillation frequency in an individual may be useful within the context of pharmacological manipulations (Muthukumaraswamy et al., 2009). In the current study, we applied for the first time both MEG and MRS measurements in healthy volunteers medicated with the highly selective serotonin re-uptake inhibitor (SSRI) antidepressant escitalopram. Fifteen right handed, healthy volunteers (seven females and eight males) with a mean age of 23.4 (range 19–40) years received escitalopram (10 mg/day) for a total of 7–10 days in open-label, not placebo controlled manner. MRS scanning following MEG measurements was performed twice: once before and once after medication in all participants on a 3T Siemens TIM Trio scanner (Erlangen, Germany) with a body coil transmitter and a 32-channel receive head array. Data were acquired from a 20 mm × 25 mm × 20 mm voxel located in the occipital cortex (OCC). The voxel was positioned manually by reference to an axial T1-weighted gradient echo image. SPECIAL data with water suppression were acquired (TE 8.5 s, 3200 ms, 16-step phase cycle, 128 averages for the occipital voxel). Following spectral processing, SPECIAL data were analysed with LCModel version 6.2-2B using a basis set that consisted of 21 simulated metabolite basis spectra. Basis spectra were generated using an in-house, MATLAB (Natick, MA, USA) based implementation of the density matrix formalism to simulate the effect of the SPECIAL pulse sequence on each of the 21 metabolite spin systems studied. For more information about the accuracy and reliability of SPECIAL in conjunction with the LCModel in the measurement of GABA concentrations see Near et al. (2013). MEG data were acquired from eight of the 15 mentioned above subjects using an Elekta Neuromag 306channel system (Elekta, Stockholm, Sweden; 204 planar gradiometers, 102 magnetometers). MEG data were first visually inspected to remove channels severely affected by noise, and then de-noised and corrected for head movements using Elekta’s Maxfilter Signal Space Separation algorithm (Taulu et al., 2004), making use of the spatiotemporal filter algorithm to detect noise components. The anatomical images from MRI were used to create single-shell MEG forward models fitted to each subject’s anatomy (Nolte, 2003). Using SPM’s spm_eeg_inv_ mesh a transformation was computed for each subject, mapping a set of canonical meshes for cortical surface, skull and scalp to the subject’s anatomy. The individual MEG forward models were derived from these transformations using Fieldtrip’s forward toolbox (Oostenveld et al., 2011). Data were band-pass filtered between 30 and 120Hz, and then transformed from sensor-space into source-space using a linearly constrained minimum variance beamformer (Woolrich et al., 2011). Each subject’s source-space map for left and right gratings was then visually inspected (using FMRIB Software Library’s viewer) to locate the voxels with the visual cortex with the strongest gamma activation to the stimulus. The gamma peak frequency was found by averaging the data over the time interval from 300ms to 1000ms relative to stimulus onset and compared with a baseline period from –1000ms to –200ms relative to the stimulus. The peak gamma frequency was found by computing the ratio between power in the stimulus period and baseline period for each frequency bin. The frequency bin with the largest relative change between baseline and stimulus period was taken as the gamma peak frequency for that subject. The effects of escitalopram treatment on MRS and MEG measurements were compared by paired t tests in SPPS v.20 (SPSS Inc., Chicago, IL, USA). A pilot study of the effect of short-term escitalopram treatment on brain metabolites and gamma-oscillations in healthy subjects