Glutamatergic Signaling Drives Ketamine-Mediated Response in Depression: Evidence from Dynamic Causal Modeling

Glutamatergic Signaling Drives Ketamine-Mediated Response in Depression: Evidence from Dynamic... Background: The glutamatergic modulator ketamine has rapid antidepressant effects in individuals with major depressive disorder and bipolar depression. Thus, modulating glutamatergic transmission may be critical to effectively treating depression, though the mechanisms by which this occurs are not fully understood. Methods: This double-blind, crossover, placebo-controlled study analyzed data from 18 drug-free major depressive disorder subjects and 18 heathy controls who received a single i.v. infusion of ketamine hydrochloride (0.5 mg/kg) as well as an i.v. saline placebo. Magnetoencephalographic recordings were collected prior to the first infusion and 6 to 9 hours after both ketamine and placebo infusions. During scanning, participants passively received tactile stimulation to the right index finger. Antidepressant response was assessed across timepoints using the Montgomery-Asberg Depression Rating Scale. Dynamic causal modeling was used to measure changes in α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA)- and N-methyl-D-aspartate (NMDA)-mediated connectivity estimates in major depressive disorder subjects and controls using a simple model of somatosensory evoked responses. Results: Both major depressive disorder and healthy subjects showed ketamine-mediated NMDA-blockade sensitization, with major depressive disorder subjects showing enhanced NMDA connectivity estimates in backward connections and controls showing enhanced NMDA connectivity estimates in forward connections in our model. Within our major depressive disorder subject group, ketamine efficacy, as measured by improved mood ratings, correlated with reduced NMDA and AMPA connectivity estimates in discrete extrinsic connections within the somatosensory cortical network. Conclusions: These findings suggest that AMPA- and NMDA-mediated glutamatergic signaling play a key role in antidepressant response to ketamine and, further, that dynamic causal modeling is a powerful tool for modeling AMPA- and NMDA-mediated connectivity in vivo. Clinicaltrials.gov: NCT#00088699. Keywords: ketamine, major depressive disorder, magnetoencephalography, dynamic causal modeling Received: February 12, 2018; Revised: March 30, 2018; Accepted: April 10, 2018 Published by Oxford University Press on behalf of CINP 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. This Open Access article contains public sector information licensed under the Open Government Licence v2.0 (http://www.nationalarchives.gov.uk/doc/ open-government-licence/version/2/). 740 Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Gilbert et al. | 741 Significance Statement This research demonstrates that ketamine administration leads to short-term changes in modeled elecytrophysiological esti- mates of NMDA-mediated connectivity in a simple model of somatosensory evoked responses in a group of patients with major depressive disorder (MDD) and healthy controls. Further, this research demonstrates that specific changes in AMPA- and NMDA- mediated extrinsic connectivity estimates correlate with change in depression scores within MDD patients following ketamine administration. These findings are the first to demonstrate changes in modeled electrophysiological glutamatergic signaling estimates in MDD patients following ketamine administration. Introduction Glutamatergic signaling abnormalities are thought to be an study found that, roughly 6.5 hours post-ketamine infusion, underlying factor in mood disorders (Yüksel and Öngür, 2010), MDD subjects who responded to ketamine had increased stim- including major depressive disorder (MDD) (Choudary et  al., ulus-evoked γ-band responses compared with nonresponders 2005; Bernard et al., 2011) and bipolar depression (Eastwood and (Cornwell et  al., 2012). These findings were thought to result Harrison, 2010). Interest in targeting this system for treatment from AMPA-mediated glutamatergic neurotransmission fol- has grown exponentially (Ohgi et al., 2015), with particular focus lowing synaptic potentiation, which could provide one explan- on the glutamatergic modulator ketamine (Zarate et  al., 2006; ation for how ketamine influences mood. Indeed, a few studies Diazgranados et al., 2010) as a clinical treatment option. Several using modeling in tandem with electrophysiology to measure studies have now demonstrated that a single subanesthetic changes in effective connectivity found changes in both AMPA dose of ketamine can rapidly relieve depressive symptoms in and NMDA signaling post-ketamine administration (Moran individuals with MDD (Zarate et al., 2006 Murr ; ough et al., 2013) et al., 2015; Muthukumaraswamy et al., 2015). Importantly, one and bipolar depression (Diazgranados et al., 2010 Zar ; ate et al., of these studies used ketamine to model schizophrenia symp- 2012), including treatment-resistant subjects. Understanding toms in rats (Moran et  al., 2015), administering much higher the mechanisms underlying ketamine’s rapid antidepressant ketamine doses than typically used to treat depressive symp- effects could help identify novel biomarkers for antidepressant toms. The second study administered subanesthetic doses of response as well as expedite the development of fast-acting and ketamine to healthy control subjects (Muthukumaraswamy more effective therapeutics to treat depressive symptoms. et al., 2015), leaving open the question of how ketamine influ- Ketamine is a noncompetitive N-methyl-D-aspartate (NMDA) ences mood in MDD subjects. receptor antagonist, although recent studies suggest that NMDA This double-blind, crossover, placebo-controlled study used antagonism may not be the mechanism underlying its anti- magnetoencephalography (MEG) in tandem with dynamic depressant effects. For instance, recent work has shown that the causal modeling (DCM) to measure AMPA and NMDA signaling ketamine metabolite (2R,6R)-hydroxynorketamine, which is not in vivo during a passive somatosensory stimulation task in both an NMDA antagonist, exerts antidepressant effects in animal MDD subjects and healthy controls post-ketamine administra- models, potentially by enhancing α-amino-3-hydroxy-5-methyl- tion. The post-ketamine condition was compared with both a 4-isoxazolepropionic acid (AMPA) throughput (Zanos et al., 2016). baseline scan collected prior to ketamine infusion and a con- Subanesthetic-dose ketamine administration leads to immedi- trol scan during which an i.v. infusion of a saline placebo was ate presynaptic disinhibition of glutamatergic neurons, produc- administered. DCM was used to estimate effective connectivity ing a glutamate surge (Moghaddam et  al., 1997). This surge is within a simple network activated by the task following keta- thought to result from the blockade of NMDA receptors targeting mine administration. DCM fits a biophysically plausible model γ-aminobutyric acid-ergic interneurons, leading to local inhib- of neural dynamics to measure electrophysiological signals. ition of interneuron tonic firing and subsequent disinhibition These models provide intrinsic (within-region) and extrinsic of glutamate transmission (Homayoun and Moghaddam, 2007). (between-region) estimates of synaptic response in specific Due to a blockade of NMDA receptors on postsynaptic excita- neuronal ensembles (Moran et  al., 2011), providing a powerful tory neurons, excess synaptic glutamate is primarily taken up means to measure brain dynamics in vivo. We were particularly by AMPA receptors, thereby activating neuroplasticity-related interested in measuring AMPA- and NMDA-mediated extrinsic signaling pathways (including mammalian target of rapamy- connectivity to compare baseline estimates (here, both base- cin complex 1 (Li et  al., 2010; Li et  al., 2011) and brain-derived line and placebo) with estimates obtained within the window of neurotrophic factor (Liu et  al., 2012), both of which result in antidepressant response to ketamine. increased synaptogenesis and synaptic potentiation). AMPA’s role is supported by studies demonstrating that administration Methods of an AMPA receptor antagonist can neutralize ketamine’s anti- depressant effects (Maeng et al., 2008K ; oike et al., 2011) in ani- Participants mal models of depression. All participants were studied at the National Institute of Mental In addition, several electrophysiological studies have dem- Health (NIMH) in Bethesda, Maryland between September 2011 onstrated that ketamine directly induces spontaneous syn γ - and August 2016. The present study used data drawn from a chrony (30–80 Hz) within cortical networks (Hong et  al., 2010; larger clinical trial (NCT#00088699) that assessed ketamine’s Lazarewicz et al., 2010; Cornwell et al., 2012Sha ; w et al., 2015). antidepressant effects (n = 60). The present study included only This increased γ synchrony is thought to result from keta- those subjects who completed all study scans; the sample com- mine-induced pyramidal cell disinhibition (Homayoun and prised 18 healthy controls (11 F/7 M, mean ag = e 33.9 ± 10.3 years) Moghaddam, 2007), thus resulting in increased pyramidal cell and 18 subjects with a DSM-IV-TR diagnosis of MDD (American excitation. While ketamine-induced disinhibition and the sub- Psychiatric Association, 1994) without psychotic features (10 sequent glutamate surge are transient phenomena, one recent Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 742 | International Journal of Neuropsychopharmacology, 2018 F/8 M, mean age = 36.9 ± 10.7  years). MDD subjects were 18 to Synthetic third-order balancing was used for active noise can- 65 years old, were experiencing a major depressive episode last- cellation. Offline, MEG data were first visually inspected, and tri- ing at least 4 weeks, and had a Montgomery-Asberg Depression als were removed where visible artifacts (e.g., head movements, Rating Scale (MADRS) (Montgomery and Asberg, 1979) score of jaw clenches, eye blinks, and muscle movements) were present. ≥20 at screening. Treatment resistance was also confirmed by In addition, individual channels showing excessive sensor noise prior failure to respond to at least one adequate antidepressant were marked as bad and removed from the analysis. Data were trial, as assessed using the Antidepressant Treatment History then bandpass filtered from 1 to 58 Hz and epoched from -100 to Form (Sackeim, 2001). Diagnosis was determined by Structured 300 milliseconds peristimulus time. The analysis routines avail- Clinical Interviews for Axis I  DSM-IV-TR Disorders–Patient able in the academic freeware SPM12 (Wellcome Trust Centre for Edition (First et  al., 2002). Healthy controls were 18 to 65  years Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/) were used for old, had no Axis I  disorder as determined by the Structured data processing. This work used the computational resources of Clinical Interviews for Axis I DSM-IV-TR Disorders-Non-Patient the NIH HPC Biowulf cluster (http://hpc.nih.gov). Edition, and had no family history of Axis I  disorders in first- degree relatives. All MDD subjects were hospitalized for the Source Localization and Source Activity Extraction duration of the study and drug-free from psychotropic medica- The multiple sparse priors routine implemented in SPM12 was tions for at least 2 weeks prior to MEG testing. Healthy controls used to identify gamma frequency (30–58 Hz) sources of activ- completed study procedures as inpatients but were otherwise ity from each participant’s sensor-level data over a peristimulus outpatients. event time window from -100 to 300 milliseconds. Gamma fre- All participants were in good health as evaluated by a med- quency was targeted, as recent findings using a similar paradigm ical history and physical examination, toxicology screens and demonstrated robust, ketamine-mediated cortical responses in urinalysis, blood laboratory results, clinical MRI, and electro- that band (Cornwell et  al., 2012). In addition, a host of studies cardiogram. The Combined Neuroscience Institutional Review have demonstrated increased gamma synchrony/power follow- Board at the NIH approved the study. All participants provided ing ketamine administration (Hong et al., 2010 Lazar ; ewicz et al., informed written consent, and MDD subjects were matched 2010; Muthukumaraswamy et al., 2015 Sha ; w et al., 2015). Evoked with an NIMH advocate from the Human Subjects Protection responses to airpuff stimulation were localized to 512 potential Unit to monitor consent and participation. mesh points using a variational Bayesian approach following co-registration of sensor positions to a canonical template brain. Clinical Measurements Participant-level activation maps were constructed following inversion of each data session (i.e., baseline, placebo, ketamine) The primary outcome measure, the MADRS (Montgomery and separately for all subjects. No prior constraints on source loca- Asberg, 1979), was administered 60 minutes prior to infusion tion were used. Following the inversion, statistical maps of (both ketamine and placebo) and at multiple time points (40, group activity were computed, and a mixed-effects ANOVA was 80, 120, and 230 minutes postinfusion as well as at Days 1, 2, 3, used to define source-localized cortical regions showing a main 10, and 11). For efficiency, we classified the preinfusion rating effect of the airpuff stimulus in the ketamine condition, thresh- as a baseline measurement, and we classified the 230-minute olded at P < .05 family-wise error correction. postinfusion rating as either the post-ketamine or post-placebo Group-level statistical activation maps demonstrated stimu- rating, depending on condition. This timepoint was chosen lus-evoked gamma-band activity in a network of brain regions because it was closest to the time of the MEG recording. Finally, including bilateral somatosensory cortices, posterior parietal we classified the Day 11 post-ketamine timepoint as the rating and middle to superior temporal sulci, and anterior/inferior for sustained post-ketamine response. Immediate change in temporal and frontal lobes (Figure 1A). Because our aim was to MADRS score was calculated by subtracting baseline and post- characterize AMPA- and NMDA-mediated extrinsic connectivity placebo ratings from post-ketamine ratings. Sustained change in a simple model of ketamine’s effects, we focused on 2 regions in MADRS scores was calculated by subtracting baseline ratings to bilaterally model forward and backward connections in our from sustained post-ketamine ratings. network: bilateral S1 and inferior frontal cortex (see Figure  1A and below for source locations). Source activity at each region of MEG Acquisition and Preprocessing interest was extracted using SPM’s source extraction algorithm using a 5-mm radius, extracting individual airpuff trials from MEG recordings were collected 2 to 4 days prior to the first infu- the initial preprocessed data. Subsequent analyses used these sion and 6 to 9 hours after both ketamine and placebo experi- ‘virtual electrode’ signals in the 1- to 58-Hz band. menter-blinded infusions. The order of ketamine and placebo infusions was randomized across participants. During each session, participants received tactile stimulation of the right Dynamic Causal Modeling index finger (500 stimuli, 25-millisecond duration, 2-Hz average rate) during a 250-second experimental run. Tactile stimula- DCM uses a biophysical model of neural responses based on tion was controlled by a pneumatic stimulating device emitting neural mass models to predict recorded electrophysiological brief bursts of air (30 psi) displacing a plastic membrane resting data (David et  al., 2006). The present study specifically used a against the skin of the distal phalange (see, e.g., Cornwell et al., conductance-based neural mass model for DCM for electro- 2012). Throughout the duration of the task, participants were physiology, the standard CMM_NMDA model as implemented in asked to focus on a stationary dot projected on a screen in the SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), to model responses subject’s field of view. between S1 and frontal cortex. Within the model, synaptic Neuromagnetic data were collected using a 275-channel CTF responses are modeled within (i.e., intrinsic) and between (i.e., system with SQUID-based axial gradiometers (VSM MedTech extrinsic) regions. Intrinsic excitatory connections are medi- Ltd.) housed in a magnetically-shielded room (Vacuumschmelze). ated by both AMPA and NMDA receptor types, while intrinsic Data were collected at 1200 Hz with a bandwidth of 0 to 300 Hz. inhibitory connections use γ-aminobutyric acid receptors. Each Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Gilbert et al. | 743 Figure 1. Source locations, model architecture, and example model fits. (A) Evoked gamma frequency (30–58 Hz) source-localized estimates of the main effect of keta- mine for all participants, thresholded at P < .05 corrected. (B) A simple model architecture was used that included subcortical inputs to left primary somatosensory cortex and lateral connections to right primary somatosensory cortex. Forward and backward recurrent extrinsic connections carried signals from S1 to frontal cortex, bilaterally. (C) Example model fits showing measured wide-band (1–42 Hz) virtual electrode signals (observed) from left S1 (blue), right S1 (red), left frontal (orange), and right frontal (purple) sources compared with the estimated signal from the fitted model (predicted). receptor type is furnished with its own time constants and were then used to initialize a second set of DCMs for each par - dynamics. Extrinsic connection parameters for both fast (AMPA- ticipant and condition, and model fits were again assessed. The mediated) and slow (NMDA-mediated) glutamatergic signaling negative free energy bound on the log-model evidence was then are mediated by 2 connection types: “feedforward” connections used to adjudicate between the first and second model for each and “feedback” connections. Within the model, superficial pyr - subject and condition, selecting the model with greater log- amidal cells encode and carry feedforward signaling to stel- model evidence for subsequent analyses. Parameter estimates late cells, while deep pyramidal cells carry feedback signaling were harvested from optimized DCMs for the winning model to both superficial pyramidal cells and inhibitory interneurons for each subject and condition separately to compare ketamine- (Figure  2A). More detailed information on this model architec- mediated effects on extrinsic AMPA- and NMDA-mediated con- ture can be found in the spm_fx_cmm_nmda.m file, freely avail- nectivity estimates. able in SPM12. Thalamic (stimulus-bound) input was modeled with a Gaussian bump function that drove activity in left S1 (-40, Statistical Analyses -32, 60) in our model. Left S1 was laterally connected with right S1 (42, -30, 62). Signals were then passed via forward connec- To first examine whether there were biases in model fits between tions from bilateral S1 to bilateral inferior frontal cortex (left: conditions, model fits for the winning DCM were computed by -46, 28, -14; right: 44, 28, -14). Backward connections from frontal correlating the estimated data from the fitted model to the cortex to S1 ensured recurrent extrinsic connections (Figure 1B). extracted virtual electrode data. The model fits were compared For the DCM analyses, MEG activity for the extracted time within and between groups using paired and 2-sample t tests. series was fitted over 1 to 300 milliseconds peristimulus time To then determine whether there were differences between in a wide-frequency band from 1 to 42 Hz using an LFP model the MDD and control groups based on condition, the extracted to capture event-related potentials of evoked activity. For com- parameter estimates for AMPA and NMDA connectivity were putational efficiency, DCM optimizes a posterior density over entered separately into mixed-effects ANOVAs. We specifically free parameters (parameterized by its mean and covariance) tested for group (controls vs MDD subjects) by condition (base- via a standard variational Bayesian inversion procedure (Friston line, placebo, and ketamine) effects. Posthoc t tests were used et al., 2007). Model inversion results in optimized parameters of to compare between- and within-group differences separately, different receptor-mediated synaptic responses given the model using Bonferonni correction to correct for multiple comparisons architecture that best predicts a given dataset (here, the virtual over connections. We subsequently tested whether the variance electrode signals in the 1- to 42-Hz band from bilateral S1 and between MDD and control estimates differed for any statistic- bilateral frontal cortex). We were specifically interested in the ally significant between-group effects identified in our posthoc optimized parameters for AMPA- and NMDA-mediated extrin- comparisons using a 2-sample F-test for equal variances. Finally, sic connectivity between our regions of interest. In the present to establish whether AMPA- and NMDA-mediated extrinsic con- analysis, initial DCMs were computed for each participant and nectivity parameter estimates from MDD subjects were related condition and model fits were assessed. The posterior estimates to change in depressive symptom scores, we computed pairwise Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 744 | International Journal of Neuropsychopharmacology, 2018 Figure 2. Dynamic causal modeling (DCM) model and NMDA-mediated effects in vivo. (A) The CMM_NMDA model included four distinct cell layers: superficial p - yr amidal cells, spiny stellates, inhibitory interneurons, and deep pyramidal cells. Superficial pyramidal cells carry forward extrinsic signals to excitatory spiny stellate cells. Deep pyramidal cells carry backward extrinsic signals to both superficial pyramidal cells and inhibitory interneurons. B. A comparison of between-subject dif- ferences in α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA)- and N-methyl-D-aspartate (NMDA)-mediated connectivity estimates found significantly increased NMDA-mediated connectivity following ketamine administration in the backward connection from right frontal cortex to right S1 in subjects with major depressive disorder (MDD) compared to controls. C. A comparison of within-subject differences in AMPA- and NMDA-mediated connectivity estimates found signifi- cantly increased NMDA-mediated connectivity following ketamine administration compared to placebo administration in the forward connection from right S1 to right frontal cortex in our controls. linear correlation coefficients between the NMDA and AMPA and no significant differences in estimated fits were observed parameter estimates from the ketamine scan and change in within groups (MDD subjects: baseline mean = 0.648 ± 0.042 SE, MADRS scores from baseline to ketamine, placebo to ketamine, placebo mean = 0.593 ± 0.053 SE, ketamine mean = 0.669 ± 0.047 and baseline to Day 11 post-ketamine. SE; controls: baseline mean= 0.699 ± 0.052 SE, placebo mean = 0.743 ± 0.051 SE, ketamine mean = 0.638 ± 0.054 SE). Model fits were also compared between groups (i.e., control vs MDD) Results for each condition using 2-sample t tests; similarly, no signifi- A multiple sparse priors routine was used to infer the genera- cant differences in model fits were found. Example model fits for tors of the MEG signal. Significant group-level evoked gamma- a single control and MDD subject are shown in Figure 1C. band activation was identified in response to the airpuff Following model comparisons, the extrinsic connectivity par - stimulus specifically in the ketamine session (Figure  1A) for ameter estimates for both fast (i.e., AMPA) and slow (i.e., NMDA) both MDD subjects and controls. The network of regions acti- glutamatergic signaling were extracted to determine if there vated following ketamine administration included robust bilat- were differences in the fitted model estimates between groups. eral responses in S1 and surrounding somatosensory cortex, Our analysis of extrinsic AMPA signaling showed no significant more posterior regions in parietal and temporal cortices, inferior effects (F (1,2) = 1, P = .3676). Our analysis of extrinsic GroupxCondition regions in the anterior temporal lobes, and inferior frontal cor - NMDA signaling showed a significant group by condition effect tex. We focused on characterizing parameter estimates of AMPA (F (1,2) = 3.8, P = .0229). To examine this further, we first GroupxCondition and NMDA signaling using DCM for electrophysiology within a asked how depression impacted NMDA signaling estimates for simple model that included bilateral regions in S1 and inferior each condition separately (i.e., control baseline vs MDD sub- frontal cortex (Figure 1B). Our decision to focus on frontal cortex ject baseline, control placebo vs MDD subject placebo, control was motivated by previous findings demonstrating changes in ketamine vs MDD subject ketamine) using t tests to compare frontal-to-parietal connectivity following ketamine administra-each pair. Posthoc tests between diagnostic groups identified a tion in healthy subjects (Muthukumaraswamy et al., 2015). single parameter estimate showing a significant between-group An iterative procedure was used to fit the data, using esti- difference; specifically, the NMDA parameter estimate for the mated model complexity to choose the winning DCM for each feedback signal from right frontal cortex to right S1 (t(34) = -2.85, subject and condition. Model fits for the winning DCM were P = .0354 corrected) during the ketamine condition showed computed by correlating the estimated data from the fitted increased NMDA-mediated backward connectivity for MDD sub- model to the extracted virtual electrode data. Model fits were jects compared with controls (Figure  2B). Subsequent compari- compared using paired t tests within MDD and control groups sons of the variance between MDD subjects and controls on this separately for all conditions (i.e., baseline, placebo, ketamine), connection over recording sessions showed unequal variance Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Gilbert et al. | 745 between groups for the baseline scan (F = 4.1, P = .0058), but not scores from both baseline to ketamine (r = 0.5241, P < .05) and for the ketamine (F= 0.83, P = .7064) or placebo (F= 1.07, P = .8896) placebo to ketamine (r = 0.6005, P < .01) (Figure  3B). This param- scans. No NMDA-mediated differences were observed between eter also approached significance with the sustained change in groups for either the baseline or placebo conditions. Posthoc t MADRS scores from baseline to Day 11 post-ketamine infusion tests between sessions within each group separately identified a (r = 0.3900, P = .05) (Figure 3B). These results indicate that reduced single parameter estimate that showed a significant ketamine- AMPA connectivity estimates between left S1 and left frontal mediated effect; specifically, the NMDA parameter estimate for cortex were correlated with improved mood scores. the forward signal from right S1 to right frontal cortex showed an increase in NMDA-mediated forward connectivity for keta- Discussion mine compared with placebo (but not baseline) for controls only (t(17) = -3.00, P = .0337 corrected; Figure 2C). No significant effects This study used MEG recordings in tandem with DCM and an for MDD subjects were observed, and no other NMDA-mediated airpuff somatosensory stimulation paradigm to investigate connectivity estimates showed ketamine-induced effects. No AMPA- and NMDA-mediated connectivity changes following AMPA-mediated connectivity estimates showed between-ses- ketamine administration in subjects with treatment-resistant sion effects for either the MDD subjects or controls. MDD and healthy controls. We found 2 distinct NMDA-mediated Finally, as an additional exploratory analysis, we sought to effects of ketamine in vivo. The first involved greater NMDA- determine whether the AMPA- and NMDA-mediated extrinsic mediated connectivity in the backward connection from right connectivity parameter estimates correlated with change in frontal cortex to right somatosensory cortex for MDD subjects MADRS scores within the MDD subject group. Here, a more lib- relative to controls following ketamine administration. The sec- eral criterion of P < .05 uncorrected was used to determine sig- ond involved an increase in NMDA-mediated connectivity in the nificance. For the NMDA estimates, a significant correlation was forward connection from right somatosensory cortex to right observed between the lateral connection from right S1 to left frontal cortex for controls following ketamine administration S1 following ketamine administration and change in MADRS compared to placebo (but not baseline). scores from both baseline to ketamine (r = 0.4118, P < .05) and Interestingly, in the first set of results, significant increases placebo to ketamine (r = 0.4013, P < .05) (Figure 3A). These results in the estimated NMDA-mediated connectivity in these indicate that reduced NMDA connectivity estimates between regions were observed, though there appeared to be distinct right and left S1 were correlated with improved mood scores. effects in MDD subjects compared with controls. In particular, For the AMPA estimates, a significant correlation was observed we found NMDA-mediated, top-down, modulatory connectiv- between the forward connection from left S1 to left frontal cor - ity differences when comparing MDD subjects with controls, tex following ketamine administration and change in MADRS while we found that ketamine administration increased the Figure  3. N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) estimates following ketamine administration and improved mood. (A) NMDA estimates following ketamine administration were compared with change in Montgomery-Asberg Depression Rating Scale (MADRS) scores at several timepoints: ketamine minus baseline, ketamine minus placebo, and 11 days post-ketamine minus baseline. A significant correlation was observed between the NMDA parameter estimate from the lateral connection between right S1 and left S1 and change in MADRS scores from baseline to ketamine and placebo to keta- mine. (B) Post-ketamine administration, AMPA estimates were compared with change in MADRS score at the same timepoints. A significant correlation was observed between the AMPA parameter estimate from the forward connection between left S1 and left frontal cortex and change in MADRS scores from baseline to ketamine and placebo to ketamine. This same parameter estimate approached significance with change in MADRS score from baseline to 11 days post-ketamine infusion (P = .05). Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 746 | International Journal of Neuropsychopharmacology, 2018 NMDA-mediated bottom-up, stimulus-driven connections in It should be noted that one key limitation of our study is that controls. Similar findings of enhanced glutamatergic connect- we could not model the acute effects of ketamine administration ivity were reported in a model of schizophrenia effects (Moran on AMPA and NMDA connectivity estimates. While this has cer - et al., 2008) and have been attributed to upregulation and sen- tainly been done using healthy subjects (Muthukumaraswamy sitization effects (McLennan, 1980; van den Pol et  al., 1996). et  al., 2015), the question of ketamine’s acute effects on MDD That is, ketamine-induced NMDA antagonism might lead to subjects will need to be examined further. short-term sensitization of postsynaptic mechanisms, affect- In conclusion, our findings demonstrate that ketamine ing forward and backward NMDA connectivity separately administration leads to key differences in NMDA- and AMPA- for both MDD subjects and healthy controls. This sensitiza- mediated connectivity estimates measured using magnetoen- tion was evident in controls who demonstrated a significant cephalography in tandem with DCM. They add to a growing body increase in NMDA-mediated connectivity, while ketamine of evidence that glutamatergic signaling differences are key to served to stabilize NMDA-mediated connectivity estimates for ketamine’s antidepressant efficacy, with AMPA receptor differ - MDD subjects (reflected by a stabilization of the variance esti- ences supporting longer-term antidepressant response in MDD mates for MDD subjects from baseline to ketamine and pla- subjects. In addition, our findings underscore the usefulness of cebo scans). DCM as a tool to model AMPA- and NMDA-mediated connectiv- Secondarily, we examined whether changes in AMPA- or ity in vivo. NMDA-mediated extrinsic connectivity estimates correlated with change in MADRS rating scale scores within our MDD group following ketamine administration. Again, we found 2 Acknowledgments distinct effects. The first was a correlation between the strength The authors thank the 7SE research unit and staff for their sup- of the lateral connection from right S1 to left S1 and depression port. We also thank Rosalyn Moran for insightful discussion and rating scale scores, where decreased NMDA-mediated connect- helpful comments. Ioline Henter (NIMH) provided invaluable ivity was related to improved mood. The second was a correl- editorial assistance. The authors are entirely responsible for the ation between the strength of the frontal connection from left scientific content of the paper. S1 and left frontal cortex and depression rating scale scores, Funding for this work was supported by the Intramural where decreased AMPA-mediated connectivity was related to Research Program at the National Institute of Mental Health, improved mood. When comparing change in depression rating National Institutes of Health (IRP-NIMH-NIH; ZIA MH002857), by scale scores across time, longer lasting effects were observed a NARSAD Independent Investigator Award to Dr Zarate, and by a for AMPA-mediated connectivity relative to NMDA connect- Brain and Behavior Mood Disorders Research Award to Dr Zarate. ivity, with the correlation between AMPA estimates post-ket- amine and improved mood approaching significance at even 11 days postinfusion. Decreases in NMDA- and AMPA-mediated connectivity have been reported elsewhere following ketamine Statement of Interest administration (Muthukumaraswamy et  al., 2015). Notably, Dr Zarate is listed as a coinventor on a patent for the use of these previous findings showed that ketamine modulated the ketamine and its metabolites in major depression and suicidal backward connections from frontal to parietal regions during ideation. Dr Zarate is listed as a co-inventor on a patent for the the resting state for both AMPA and NMDA. Our findings build use of (2R,6R)-hydroxynorketamine, (S)-dehydronorketamine, on this work by showing that ketamine-mediated modulation and other stereoisomeric dehydro and hydroxylated metabo- of early lateral connections for NMDA and forward connections lites of (R,S)-ketamine metabolites in the treatment of depres- for AMPA correlated with improvements in mood in individu- sion and neuropathic pain. Dr Zarate is listed as co-inventor on als with treatment-resistant MDD. Thus, reductions in both a patent application for the use of (2R,6R)-hydroxynorketamine AMPA- and NMDA-mediated connectivity following ketamine and (2S,6S)-hydroxynorketamine in the treatment of depression, administration lead to positive behavioral outcomes in MDD anxiety, anhedonia, suicidal ideation, and posttraumatic stress subjects. disorders; he has assigned his patent rights to the U.S. govern- Taken together, these findings suggest that post-ketamine ment but will share a percentage of any royalties that may be administration, NMDA-mediated glutamatergic sensitivity was received by the government. All other authors have no conflict evident in MEG recordings collected 6 to 9 hours postinfusion, as of interest to disclose, financial or otherwise. demonstrated by increased NMDA-mediated extrinsic connect- ivity estimates in healthy controls and stabilization of NMDA- mediated extrinsic connectivity estimates (i.e, no differences References in variance) in MDD subjects. 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McLennan H (1980) The effect of decortication on the excita- Zarate CA Jr, Brutsche NE, Ibrahim L, Franco-Chaves J, tory amino acid sensitivity of striatal neurones. Neurosci Lett Diazgranados N, Cravchik A, Selter J, Marquardt CA, Liberty 18:313–316. V, Luckenbaugh DA (2012) Replication of Ketamine’s anti- Moghaddam B, Adams B, Verma A, Daly D (1997) Activation of depressant efficacy in bipolar depression: a randomized con- glutamatergic neurotransmission by ketamine: a novel step trolled add-on trial. Biol Psychiatry 71:939–946. Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Neuropsychopharmacology Oxford University Press

Glutamatergic Signaling Drives Ketamine-Mediated Response in Depression: Evidence from Dynamic Causal Modeling

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Abstract

Background: The glutamatergic modulator ketamine has rapid antidepressant effects in individuals with major depressive disorder and bipolar depression. Thus, modulating glutamatergic transmission may be critical to effectively treating depression, though the mechanisms by which this occurs are not fully understood. Methods: This double-blind, crossover, placebo-controlled study analyzed data from 18 drug-free major depressive disorder subjects and 18 heathy controls who received a single i.v. infusion of ketamine hydrochloride (0.5 mg/kg) as well as an i.v. saline placebo. Magnetoencephalographic recordings were collected prior to the first infusion and 6 to 9 hours after both ketamine and placebo infusions. During scanning, participants passively received tactile stimulation to the right index finger. Antidepressant response was assessed across timepoints using the Montgomery-Asberg Depression Rating Scale. Dynamic causal modeling was used to measure changes in α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA)- and N-methyl-D-aspartate (NMDA)-mediated connectivity estimates in major depressive disorder subjects and controls using a simple model of somatosensory evoked responses. Results: Both major depressive disorder and healthy subjects showed ketamine-mediated NMDA-blockade sensitization, with major depressive disorder subjects showing enhanced NMDA connectivity estimates in backward connections and controls showing enhanced NMDA connectivity estimates in forward connections in our model. Within our major depressive disorder subject group, ketamine efficacy, as measured by improved mood ratings, correlated with reduced NMDA and AMPA connectivity estimates in discrete extrinsic connections within the somatosensory cortical network. Conclusions: These findings suggest that AMPA- and NMDA-mediated glutamatergic signaling play a key role in antidepressant response to ketamine and, further, that dynamic causal modeling is a powerful tool for modeling AMPA- and NMDA-mediated connectivity in vivo. Clinicaltrials.gov: NCT#00088699. Keywords: ketamine, major depressive disorder, magnetoencephalography, dynamic causal modeling Received: February 12, 2018; Revised: March 30, 2018; Accepted: April 10, 2018 Published by Oxford University Press on behalf of CINP 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. This Open Access article contains public sector information licensed under the Open Government Licence v2.0 (http://www.nationalarchives.gov.uk/doc/ open-government-licence/version/2/). 740 Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Gilbert et al. | 741 Significance Statement This research demonstrates that ketamine administration leads to short-term changes in modeled elecytrophysiological esti- mates of NMDA-mediated connectivity in a simple model of somatosensory evoked responses in a group of patients with major depressive disorder (MDD) and healthy controls. Further, this research demonstrates that specific changes in AMPA- and NMDA- mediated extrinsic connectivity estimates correlate with change in depression scores within MDD patients following ketamine administration. These findings are the first to demonstrate changes in modeled electrophysiological glutamatergic signaling estimates in MDD patients following ketamine administration. Introduction Glutamatergic signaling abnormalities are thought to be an study found that, roughly 6.5 hours post-ketamine infusion, underlying factor in mood disorders (Yüksel and Öngür, 2010), MDD subjects who responded to ketamine had increased stim- including major depressive disorder (MDD) (Choudary et  al., ulus-evoked γ-band responses compared with nonresponders 2005; Bernard et al., 2011) and bipolar depression (Eastwood and (Cornwell et  al., 2012). These findings were thought to result Harrison, 2010). Interest in targeting this system for treatment from AMPA-mediated glutamatergic neurotransmission fol- has grown exponentially (Ohgi et al., 2015), with particular focus lowing synaptic potentiation, which could provide one explan- on the glutamatergic modulator ketamine (Zarate et  al., 2006; ation for how ketamine influences mood. Indeed, a few studies Diazgranados et al., 2010) as a clinical treatment option. Several using modeling in tandem with electrophysiology to measure studies have now demonstrated that a single subanesthetic changes in effective connectivity found changes in both AMPA dose of ketamine can rapidly relieve depressive symptoms in and NMDA signaling post-ketamine administration (Moran individuals with MDD (Zarate et al., 2006 Murr ; ough et al., 2013) et al., 2015; Muthukumaraswamy et al., 2015). Importantly, one and bipolar depression (Diazgranados et al., 2010 Zar ; ate et al., of these studies used ketamine to model schizophrenia symp- 2012), including treatment-resistant subjects. Understanding toms in rats (Moran et  al., 2015), administering much higher the mechanisms underlying ketamine’s rapid antidepressant ketamine doses than typically used to treat depressive symp- effects could help identify novel biomarkers for antidepressant toms. The second study administered subanesthetic doses of response as well as expedite the development of fast-acting and ketamine to healthy control subjects (Muthukumaraswamy more effective therapeutics to treat depressive symptoms. et al., 2015), leaving open the question of how ketamine influ- Ketamine is a noncompetitive N-methyl-D-aspartate (NMDA) ences mood in MDD subjects. receptor antagonist, although recent studies suggest that NMDA This double-blind, crossover, placebo-controlled study used antagonism may not be the mechanism underlying its anti- magnetoencephalography (MEG) in tandem with dynamic depressant effects. For instance, recent work has shown that the causal modeling (DCM) to measure AMPA and NMDA signaling ketamine metabolite (2R,6R)-hydroxynorketamine, which is not in vivo during a passive somatosensory stimulation task in both an NMDA antagonist, exerts antidepressant effects in animal MDD subjects and healthy controls post-ketamine administra- models, potentially by enhancing α-amino-3-hydroxy-5-methyl- tion. The post-ketamine condition was compared with both a 4-isoxazolepropionic acid (AMPA) throughput (Zanos et al., 2016). baseline scan collected prior to ketamine infusion and a con- Subanesthetic-dose ketamine administration leads to immedi- trol scan during which an i.v. infusion of a saline placebo was ate presynaptic disinhibition of glutamatergic neurons, produc- administered. DCM was used to estimate effective connectivity ing a glutamate surge (Moghaddam et  al., 1997). This surge is within a simple network activated by the task following keta- thought to result from the blockade of NMDA receptors targeting mine administration. DCM fits a biophysically plausible model γ-aminobutyric acid-ergic interneurons, leading to local inhib- of neural dynamics to measure electrophysiological signals. ition of interneuron tonic firing and subsequent disinhibition These models provide intrinsic (within-region) and extrinsic of glutamate transmission (Homayoun and Moghaddam, 2007). (between-region) estimates of synaptic response in specific Due to a blockade of NMDA receptors on postsynaptic excita- neuronal ensembles (Moran et  al., 2011), providing a powerful tory neurons, excess synaptic glutamate is primarily taken up means to measure brain dynamics in vivo. We were particularly by AMPA receptors, thereby activating neuroplasticity-related interested in measuring AMPA- and NMDA-mediated extrinsic signaling pathways (including mammalian target of rapamy- connectivity to compare baseline estimates (here, both base- cin complex 1 (Li et  al., 2010; Li et  al., 2011) and brain-derived line and placebo) with estimates obtained within the window of neurotrophic factor (Liu et  al., 2012), both of which result in antidepressant response to ketamine. increased synaptogenesis and synaptic potentiation). AMPA’s role is supported by studies demonstrating that administration Methods of an AMPA receptor antagonist can neutralize ketamine’s anti- depressant effects (Maeng et al., 2008K ; oike et al., 2011) in ani- Participants mal models of depression. All participants were studied at the National Institute of Mental In addition, several electrophysiological studies have dem- Health (NIMH) in Bethesda, Maryland between September 2011 onstrated that ketamine directly induces spontaneous syn γ - and August 2016. The present study used data drawn from a chrony (30–80 Hz) within cortical networks (Hong et  al., 2010; larger clinical trial (NCT#00088699) that assessed ketamine’s Lazarewicz et al., 2010; Cornwell et al., 2012Sha ; w et al., 2015). antidepressant effects (n = 60). The present study included only This increased γ synchrony is thought to result from keta- those subjects who completed all study scans; the sample com- mine-induced pyramidal cell disinhibition (Homayoun and prised 18 healthy controls (11 F/7 M, mean ag = e 33.9 ± 10.3 years) Moghaddam, 2007), thus resulting in increased pyramidal cell and 18 subjects with a DSM-IV-TR diagnosis of MDD (American excitation. While ketamine-induced disinhibition and the sub- Psychiatric Association, 1994) without psychotic features (10 sequent glutamate surge are transient phenomena, one recent Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 742 | International Journal of Neuropsychopharmacology, 2018 F/8 M, mean age = 36.9 ± 10.7  years). MDD subjects were 18 to Synthetic third-order balancing was used for active noise can- 65 years old, were experiencing a major depressive episode last- cellation. Offline, MEG data were first visually inspected, and tri- ing at least 4 weeks, and had a Montgomery-Asberg Depression als were removed where visible artifacts (e.g., head movements, Rating Scale (MADRS) (Montgomery and Asberg, 1979) score of jaw clenches, eye blinks, and muscle movements) were present. ≥20 at screening. Treatment resistance was also confirmed by In addition, individual channels showing excessive sensor noise prior failure to respond to at least one adequate antidepressant were marked as bad and removed from the analysis. Data were trial, as assessed using the Antidepressant Treatment History then bandpass filtered from 1 to 58 Hz and epoched from -100 to Form (Sackeim, 2001). Diagnosis was determined by Structured 300 milliseconds peristimulus time. The analysis routines avail- Clinical Interviews for Axis I  DSM-IV-TR Disorders–Patient able in the academic freeware SPM12 (Wellcome Trust Centre for Edition (First et  al., 2002). Healthy controls were 18 to 65  years Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/) were used for old, had no Axis I  disorder as determined by the Structured data processing. This work used the computational resources of Clinical Interviews for Axis I DSM-IV-TR Disorders-Non-Patient the NIH HPC Biowulf cluster (http://hpc.nih.gov). Edition, and had no family history of Axis I  disorders in first- degree relatives. All MDD subjects were hospitalized for the Source Localization and Source Activity Extraction duration of the study and drug-free from psychotropic medica- The multiple sparse priors routine implemented in SPM12 was tions for at least 2 weeks prior to MEG testing. Healthy controls used to identify gamma frequency (30–58 Hz) sources of activ- completed study procedures as inpatients but were otherwise ity from each participant’s sensor-level data over a peristimulus outpatients. event time window from -100 to 300 milliseconds. Gamma fre- All participants were in good health as evaluated by a med- quency was targeted, as recent findings using a similar paradigm ical history and physical examination, toxicology screens and demonstrated robust, ketamine-mediated cortical responses in urinalysis, blood laboratory results, clinical MRI, and electro- that band (Cornwell et  al., 2012). In addition, a host of studies cardiogram. The Combined Neuroscience Institutional Review have demonstrated increased gamma synchrony/power follow- Board at the NIH approved the study. All participants provided ing ketamine administration (Hong et al., 2010 Lazar ; ewicz et al., informed written consent, and MDD subjects were matched 2010; Muthukumaraswamy et al., 2015 Sha ; w et al., 2015). Evoked with an NIMH advocate from the Human Subjects Protection responses to airpuff stimulation were localized to 512 potential Unit to monitor consent and participation. mesh points using a variational Bayesian approach following co-registration of sensor positions to a canonical template brain. Clinical Measurements Participant-level activation maps were constructed following inversion of each data session (i.e., baseline, placebo, ketamine) The primary outcome measure, the MADRS (Montgomery and separately for all subjects. No prior constraints on source loca- Asberg, 1979), was administered 60 minutes prior to infusion tion were used. Following the inversion, statistical maps of (both ketamine and placebo) and at multiple time points (40, group activity were computed, and a mixed-effects ANOVA was 80, 120, and 230 minutes postinfusion as well as at Days 1, 2, 3, used to define source-localized cortical regions showing a main 10, and 11). For efficiency, we classified the preinfusion rating effect of the airpuff stimulus in the ketamine condition, thresh- as a baseline measurement, and we classified the 230-minute olded at P < .05 family-wise error correction. postinfusion rating as either the post-ketamine or post-placebo Group-level statistical activation maps demonstrated stimu- rating, depending on condition. This timepoint was chosen lus-evoked gamma-band activity in a network of brain regions because it was closest to the time of the MEG recording. Finally, including bilateral somatosensory cortices, posterior parietal we classified the Day 11 post-ketamine timepoint as the rating and middle to superior temporal sulci, and anterior/inferior for sustained post-ketamine response. Immediate change in temporal and frontal lobes (Figure 1A). Because our aim was to MADRS score was calculated by subtracting baseline and post- characterize AMPA- and NMDA-mediated extrinsic connectivity placebo ratings from post-ketamine ratings. Sustained change in a simple model of ketamine’s effects, we focused on 2 regions in MADRS scores was calculated by subtracting baseline ratings to bilaterally model forward and backward connections in our from sustained post-ketamine ratings. network: bilateral S1 and inferior frontal cortex (see Figure  1A and below for source locations). Source activity at each region of MEG Acquisition and Preprocessing interest was extracted using SPM’s source extraction algorithm using a 5-mm radius, extracting individual airpuff trials from MEG recordings were collected 2 to 4 days prior to the first infu- the initial preprocessed data. Subsequent analyses used these sion and 6 to 9 hours after both ketamine and placebo experi- ‘virtual electrode’ signals in the 1- to 58-Hz band. menter-blinded infusions. The order of ketamine and placebo infusions was randomized across participants. During each session, participants received tactile stimulation of the right Dynamic Causal Modeling index finger (500 stimuli, 25-millisecond duration, 2-Hz average rate) during a 250-second experimental run. Tactile stimula- DCM uses a biophysical model of neural responses based on tion was controlled by a pneumatic stimulating device emitting neural mass models to predict recorded electrophysiological brief bursts of air (30 psi) displacing a plastic membrane resting data (David et  al., 2006). The present study specifically used a against the skin of the distal phalange (see, e.g., Cornwell et al., conductance-based neural mass model for DCM for electro- 2012). Throughout the duration of the task, participants were physiology, the standard CMM_NMDA model as implemented in asked to focus on a stationary dot projected on a screen in the SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), to model responses subject’s field of view. between S1 and frontal cortex. Within the model, synaptic Neuromagnetic data were collected using a 275-channel CTF responses are modeled within (i.e., intrinsic) and between (i.e., system with SQUID-based axial gradiometers (VSM MedTech extrinsic) regions. Intrinsic excitatory connections are medi- Ltd.) housed in a magnetically-shielded room (Vacuumschmelze). ated by both AMPA and NMDA receptor types, while intrinsic Data were collected at 1200 Hz with a bandwidth of 0 to 300 Hz. inhibitory connections use γ-aminobutyric acid receptors. Each Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Gilbert et al. | 743 Figure 1. Source locations, model architecture, and example model fits. (A) Evoked gamma frequency (30–58 Hz) source-localized estimates of the main effect of keta- mine for all participants, thresholded at P < .05 corrected. (B) A simple model architecture was used that included subcortical inputs to left primary somatosensory cortex and lateral connections to right primary somatosensory cortex. Forward and backward recurrent extrinsic connections carried signals from S1 to frontal cortex, bilaterally. (C) Example model fits showing measured wide-band (1–42 Hz) virtual electrode signals (observed) from left S1 (blue), right S1 (red), left frontal (orange), and right frontal (purple) sources compared with the estimated signal from the fitted model (predicted). receptor type is furnished with its own time constants and were then used to initialize a second set of DCMs for each par - dynamics. Extrinsic connection parameters for both fast (AMPA- ticipant and condition, and model fits were again assessed. The mediated) and slow (NMDA-mediated) glutamatergic signaling negative free energy bound on the log-model evidence was then are mediated by 2 connection types: “feedforward” connections used to adjudicate between the first and second model for each and “feedback” connections. Within the model, superficial pyr - subject and condition, selecting the model with greater log- amidal cells encode and carry feedforward signaling to stel- model evidence for subsequent analyses. Parameter estimates late cells, while deep pyramidal cells carry feedback signaling were harvested from optimized DCMs for the winning model to both superficial pyramidal cells and inhibitory interneurons for each subject and condition separately to compare ketamine- (Figure  2A). More detailed information on this model architec- mediated effects on extrinsic AMPA- and NMDA-mediated con- ture can be found in the spm_fx_cmm_nmda.m file, freely avail- nectivity estimates. able in SPM12. Thalamic (stimulus-bound) input was modeled with a Gaussian bump function that drove activity in left S1 (-40, Statistical Analyses -32, 60) in our model. Left S1 was laterally connected with right S1 (42, -30, 62). Signals were then passed via forward connec- To first examine whether there were biases in model fits between tions from bilateral S1 to bilateral inferior frontal cortex (left: conditions, model fits for the winning DCM were computed by -46, 28, -14; right: 44, 28, -14). Backward connections from frontal correlating the estimated data from the fitted model to the cortex to S1 ensured recurrent extrinsic connections (Figure 1B). extracted virtual electrode data. The model fits were compared For the DCM analyses, MEG activity for the extracted time within and between groups using paired and 2-sample t tests. series was fitted over 1 to 300 milliseconds peristimulus time To then determine whether there were differences between in a wide-frequency band from 1 to 42 Hz using an LFP model the MDD and control groups based on condition, the extracted to capture event-related potentials of evoked activity. For com- parameter estimates for AMPA and NMDA connectivity were putational efficiency, DCM optimizes a posterior density over entered separately into mixed-effects ANOVAs. We specifically free parameters (parameterized by its mean and covariance) tested for group (controls vs MDD subjects) by condition (base- via a standard variational Bayesian inversion procedure (Friston line, placebo, and ketamine) effects. Posthoc t tests were used et al., 2007). Model inversion results in optimized parameters of to compare between- and within-group differences separately, different receptor-mediated synaptic responses given the model using Bonferonni correction to correct for multiple comparisons architecture that best predicts a given dataset (here, the virtual over connections. We subsequently tested whether the variance electrode signals in the 1- to 42-Hz band from bilateral S1 and between MDD and control estimates differed for any statistic- bilateral frontal cortex). We were specifically interested in the ally significant between-group effects identified in our posthoc optimized parameters for AMPA- and NMDA-mediated extrin- comparisons using a 2-sample F-test for equal variances. Finally, sic connectivity between our regions of interest. In the present to establish whether AMPA- and NMDA-mediated extrinsic con- analysis, initial DCMs were computed for each participant and nectivity parameter estimates from MDD subjects were related condition and model fits were assessed. The posterior estimates to change in depressive symptom scores, we computed pairwise Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 744 | International Journal of Neuropsychopharmacology, 2018 Figure 2. Dynamic causal modeling (DCM) model and NMDA-mediated effects in vivo. (A) The CMM_NMDA model included four distinct cell layers: superficial p - yr amidal cells, spiny stellates, inhibitory interneurons, and deep pyramidal cells. Superficial pyramidal cells carry forward extrinsic signals to excitatory spiny stellate cells. Deep pyramidal cells carry backward extrinsic signals to both superficial pyramidal cells and inhibitory interneurons. B. A comparison of between-subject dif- ferences in α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA)- and N-methyl-D-aspartate (NMDA)-mediated connectivity estimates found significantly increased NMDA-mediated connectivity following ketamine administration in the backward connection from right frontal cortex to right S1 in subjects with major depressive disorder (MDD) compared to controls. C. A comparison of within-subject differences in AMPA- and NMDA-mediated connectivity estimates found signifi- cantly increased NMDA-mediated connectivity following ketamine administration compared to placebo administration in the forward connection from right S1 to right frontal cortex in our controls. linear correlation coefficients between the NMDA and AMPA and no significant differences in estimated fits were observed parameter estimates from the ketamine scan and change in within groups (MDD subjects: baseline mean = 0.648 ± 0.042 SE, MADRS scores from baseline to ketamine, placebo to ketamine, placebo mean = 0.593 ± 0.053 SE, ketamine mean = 0.669 ± 0.047 and baseline to Day 11 post-ketamine. SE; controls: baseline mean= 0.699 ± 0.052 SE, placebo mean = 0.743 ± 0.051 SE, ketamine mean = 0.638 ± 0.054 SE). Model fits were also compared between groups (i.e., control vs MDD) Results for each condition using 2-sample t tests; similarly, no signifi- A multiple sparse priors routine was used to infer the genera- cant differences in model fits were found. Example model fits for tors of the MEG signal. Significant group-level evoked gamma- a single control and MDD subject are shown in Figure 1C. band activation was identified in response to the airpuff Following model comparisons, the extrinsic connectivity par - stimulus specifically in the ketamine session (Figure  1A) for ameter estimates for both fast (i.e., AMPA) and slow (i.e., NMDA) both MDD subjects and controls. The network of regions acti- glutamatergic signaling were extracted to determine if there vated following ketamine administration included robust bilat- were differences in the fitted model estimates between groups. eral responses in S1 and surrounding somatosensory cortex, Our analysis of extrinsic AMPA signaling showed no significant more posterior regions in parietal and temporal cortices, inferior effects (F (1,2) = 1, P = .3676). Our analysis of extrinsic GroupxCondition regions in the anterior temporal lobes, and inferior frontal cor - NMDA signaling showed a significant group by condition effect tex. We focused on characterizing parameter estimates of AMPA (F (1,2) = 3.8, P = .0229). To examine this further, we first GroupxCondition and NMDA signaling using DCM for electrophysiology within a asked how depression impacted NMDA signaling estimates for simple model that included bilateral regions in S1 and inferior each condition separately (i.e., control baseline vs MDD sub- frontal cortex (Figure 1B). Our decision to focus on frontal cortex ject baseline, control placebo vs MDD subject placebo, control was motivated by previous findings demonstrating changes in ketamine vs MDD subject ketamine) using t tests to compare frontal-to-parietal connectivity following ketamine administra-each pair. Posthoc tests between diagnostic groups identified a tion in healthy subjects (Muthukumaraswamy et al., 2015). single parameter estimate showing a significant between-group An iterative procedure was used to fit the data, using esti- difference; specifically, the NMDA parameter estimate for the mated model complexity to choose the winning DCM for each feedback signal from right frontal cortex to right S1 (t(34) = -2.85, subject and condition. Model fits for the winning DCM were P = .0354 corrected) during the ketamine condition showed computed by correlating the estimated data from the fitted increased NMDA-mediated backward connectivity for MDD sub- model to the extracted virtual electrode data. Model fits were jects compared with controls (Figure  2B). Subsequent compari- compared using paired t tests within MDD and control groups sons of the variance between MDD subjects and controls on this separately for all conditions (i.e., baseline, placebo, ketamine), connection over recording sessions showed unequal variance Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Gilbert et al. | 745 between groups for the baseline scan (F = 4.1, P = .0058), but not scores from both baseline to ketamine (r = 0.5241, P < .05) and for the ketamine (F= 0.83, P = .7064) or placebo (F= 1.07, P = .8896) placebo to ketamine (r = 0.6005, P < .01) (Figure  3B). This param- scans. No NMDA-mediated differences were observed between eter also approached significance with the sustained change in groups for either the baseline or placebo conditions. Posthoc t MADRS scores from baseline to Day 11 post-ketamine infusion tests between sessions within each group separately identified a (r = 0.3900, P = .05) (Figure 3B). These results indicate that reduced single parameter estimate that showed a significant ketamine- AMPA connectivity estimates between left S1 and left frontal mediated effect; specifically, the NMDA parameter estimate for cortex were correlated with improved mood scores. the forward signal from right S1 to right frontal cortex showed an increase in NMDA-mediated forward connectivity for keta- Discussion mine compared with placebo (but not baseline) for controls only (t(17) = -3.00, P = .0337 corrected; Figure 2C). No significant effects This study used MEG recordings in tandem with DCM and an for MDD subjects were observed, and no other NMDA-mediated airpuff somatosensory stimulation paradigm to investigate connectivity estimates showed ketamine-induced effects. No AMPA- and NMDA-mediated connectivity changes following AMPA-mediated connectivity estimates showed between-ses- ketamine administration in subjects with treatment-resistant sion effects for either the MDD subjects or controls. MDD and healthy controls. We found 2 distinct NMDA-mediated Finally, as an additional exploratory analysis, we sought to effects of ketamine in vivo. The first involved greater NMDA- determine whether the AMPA- and NMDA-mediated extrinsic mediated connectivity in the backward connection from right connectivity parameter estimates correlated with change in frontal cortex to right somatosensory cortex for MDD subjects MADRS scores within the MDD subject group. Here, a more lib- relative to controls following ketamine administration. The sec- eral criterion of P < .05 uncorrected was used to determine sig- ond involved an increase in NMDA-mediated connectivity in the nificance. For the NMDA estimates, a significant correlation was forward connection from right somatosensory cortex to right observed between the lateral connection from right S1 to left frontal cortex for controls following ketamine administration S1 following ketamine administration and change in MADRS compared to placebo (but not baseline). scores from both baseline to ketamine (r = 0.4118, P < .05) and Interestingly, in the first set of results, significant increases placebo to ketamine (r = 0.4013, P < .05) (Figure 3A). These results in the estimated NMDA-mediated connectivity in these indicate that reduced NMDA connectivity estimates between regions were observed, though there appeared to be distinct right and left S1 were correlated with improved mood scores. effects in MDD subjects compared with controls. In particular, For the AMPA estimates, a significant correlation was observed we found NMDA-mediated, top-down, modulatory connectiv- between the forward connection from left S1 to left frontal cor - ity differences when comparing MDD subjects with controls, tex following ketamine administration and change in MADRS while we found that ketamine administration increased the Figure  3. N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) estimates following ketamine administration and improved mood. (A) NMDA estimates following ketamine administration were compared with change in Montgomery-Asberg Depression Rating Scale (MADRS) scores at several timepoints: ketamine minus baseline, ketamine minus placebo, and 11 days post-ketamine minus baseline. A significant correlation was observed between the NMDA parameter estimate from the lateral connection between right S1 and left S1 and change in MADRS scores from baseline to ketamine and placebo to keta- mine. (B) Post-ketamine administration, AMPA estimates were compared with change in MADRS score at the same timepoints. A significant correlation was observed between the AMPA parameter estimate from the forward connection between left S1 and left frontal cortex and change in MADRS scores from baseline to ketamine and placebo to ketamine. This same parameter estimate approached significance with change in MADRS score from baseline to 11 days post-ketamine infusion (P = .05). Downloaded from https://academic.oup.com/ijnp/article-abstract/21/8/740/4969995 by Ed 'DeepDyve' Gillespie user on 07 August 2018 746 | International Journal of Neuropsychopharmacology, 2018 NMDA-mediated bottom-up, stimulus-driven connections in It should be noted that one key limitation of our study is that controls. Similar findings of enhanced glutamatergic connect- we could not model the acute effects of ketamine administration ivity were reported in a model of schizophrenia effects (Moran on AMPA and NMDA connectivity estimates. While this has cer - et al., 2008) and have been attributed to upregulation and sen- tainly been done using healthy subjects (Muthukumaraswamy sitization effects (McLennan, 1980; van den Pol et  al., 1996). et  al., 2015), the question of ketamine’s acute effects on MDD That is, ketamine-induced NMDA antagonism might lead to subjects will need to be examined further. short-term sensitization of postsynaptic mechanisms, affect- In conclusion, our findings demonstrate that ketamine ing forward and backward NMDA connectivity separately administration leads to key differences in NMDA- and AMPA- for both MDD subjects and healthy controls. This sensitiza- mediated connectivity estimates measured using magnetoen- tion was evident in controls who demonstrated a significant cephalography in tandem with DCM. They add to a growing body increase in NMDA-mediated connectivity, while ketamine of evidence that glutamatergic signaling differences are key to served to stabilize NMDA-mediated connectivity estimates for ketamine’s antidepressant efficacy, with AMPA receptor differ - MDD subjects (reflected by a stabilization of the variance esti- ences supporting longer-term antidepressant response in MDD mates for MDD subjects from baseline to ketamine and pla- subjects. In addition, our findings underscore the usefulness of cebo scans). DCM as a tool to model AMPA- and NMDA-mediated connectiv- Secondarily, we examined whether changes in AMPA- or ity in vivo. NMDA-mediated extrinsic connectivity estimates correlated with change in MADRS rating scale scores within our MDD group following ketamine administration. Again, we found 2 Acknowledgments distinct effects. The first was a correlation between the strength The authors thank the 7SE research unit and staff for their sup- of the lateral connection from right S1 to left S1 and depression port. We also thank Rosalyn Moran for insightful discussion and rating scale scores, where decreased NMDA-mediated connect- helpful comments. Ioline Henter (NIMH) provided invaluable ivity was related to improved mood. The second was a correl- editorial assistance. The authors are entirely responsible for the ation between the strength of the frontal connection from left scientific content of the paper. S1 and left frontal cortex and depression rating scale scores, Funding for this work was supported by the Intramural where decreased AMPA-mediated connectivity was related to Research Program at the National Institute of Mental Health, improved mood. When comparing change in depression rating National Institutes of Health (IRP-NIMH-NIH; ZIA MH002857), by scale scores across time, longer lasting effects were observed a NARSAD Independent Investigator Award to Dr Zarate, and by a for AMPA-mediated connectivity relative to NMDA connect- Brain and Behavior Mood Disorders Research Award to Dr Zarate. ivity, with the correlation between AMPA estimates post-ket- amine and improved mood approaching significance at even 11 days postinfusion. Decreases in NMDA- and AMPA-mediated connectivity have been reported elsewhere following ketamine Statement of Interest administration (Muthukumaraswamy et  al., 2015). Notably, Dr Zarate is listed as a coinventor on a patent for the use of these previous findings showed that ketamine modulated the ketamine and its metabolites in major depression and suicidal backward connections from frontal to parietal regions during ideation. Dr Zarate is listed as a co-inventor on a patent for the the resting state for both AMPA and NMDA. Our findings build use of (2R,6R)-hydroxynorketamine, (S)-dehydronorketamine, on this work by showing that ketamine-mediated modulation and other stereoisomeric dehydro and hydroxylated metabo- of early lateral connections for NMDA and forward connections lites of (R,S)-ketamine metabolites in the treatment of depres- for AMPA correlated with improvements in mood in individu- sion and neuropathic pain. Dr Zarate is listed as co-inventor on als with treatment-resistant MDD. Thus, reductions in both a patent application for the use of (2R,6R)-hydroxynorketamine AMPA- and NMDA-mediated connectivity following ketamine and (2S,6S)-hydroxynorketamine in the treatment of depression, administration lead to positive behavioral outcomes in MDD anxiety, anhedonia, suicidal ideation, and posttraumatic stress subjects. disorders; he has assigned his patent rights to the U.S. govern- Taken together, these findings suggest that post-ketamine ment but will share a percentage of any royalties that may be administration, NMDA-mediated glutamatergic sensitivity was received by the government. All other authors have no conflict evident in MEG recordings collected 6 to 9 hours postinfusion, as of interest to disclose, financial or otherwise. demonstrated by increased NMDA-mediated extrinsic connect- ivity estimates in healthy controls and stabilization of NMDA- mediated extrinsic connectivity estimates (i.e, no differences References in variance) in MDD subjects. 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International Journal of NeuropsychopharmacologyOxford University Press

Published: Aug 1, 2018

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