Gamma and Delta Neural Oscillations and Association with
Clinical Symptoms under Subanesthetic Ketamine
L Elliot Hong*,1, Ann Summerfelt1, Robert W Buchanan1, Patricio O’Donnell2, Gunvant K Thaker1,
Martin A Weiler1and Adrienne C Lahti1,3
1Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA;2Department
of Anatomy and Neurobiology and Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA;3Department of Psychiatry and
Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
Several electrical neural oscillatory abnormalities have been associated with schizophrenia, although the underlying mechanisms of these
oscillatory problems are unclear. Animal studies suggest that one of the key mechanisms of neural oscillations is through glutamatergic
regulation; therefore, neural oscillations may provide a valuable animal–clinical interface on studying glutamatergic dysfunction in
schizophrenia. To identify glutamatergic control of neural oscillation relevant to human subjects, we studied the effects of ketamine, an
N-methyl-D-aspartate antagonist that can mimic some clinical aspects of schizophrenia, on auditory-evoked neural oscillations using a
paired-click paradigm. This was a double-blind, placebo-controlled, crossover study of ketamine vs saline infusion on 10 healthy subjects.
Clinically, infusion of ketamine in subanesthetic dose significantly increased thought disorder, withdrawal–retardation, and dissociative
symptoms. Ketamine significantly augmented high-frequency oscillations (gamma band at 40–85Hz, p¼0.006) and reduced low-
frequency oscillations (delta band at 1–5Hz, po0.001) compared with placebo. Importantly, the combined effect of increased gamma
and reduced delta frequency oscillations was significantly associated with more withdrawal–retardation symptoms experienced during
ketamine administration (p¼0.02). Ketamine also reduced gating of the theta-alpha (5–12Hz) range oscillation, an effect that mimics
previously described deficits in schizophrenia patients and their first-degree relatives. In conclusion, acute ketamine appeared to mimic
some aspects of neural oscillatory deficits in schizophrenia, and showed an opposite effect on scalp-recorded gamma vs low-frequency
oscillations. These electrical oscillatory indexes of subanesthetic ketamine can be potentially used to cross-examine glutamatergic
pharmacological effects in translational animal and human studies.
Neuropsychopharmacology (2010) 35, 632–640; doi:10.1038/npp.2009.168; published online 4 November 2009
Keywords: NMDA; GABA; glutamate; negative symptom; schizophrenia; sensory gating
Several electrical neural oscillatory abnormalities have been
associated with schizophrenia. Many studies have focused
on the gamma band, and in general identified gamma
reduction in power or phase in schizophrenia (Kwon et al,
1999; Kissler et al, 2000; Lee et al, 2001; Gallinat et al, 2004;
Symond et al, 2005; Cho et al, 2006). However, contrasting
findings of increased gamma activities in schizophrenia also
exist (Basar-Eroglu et al, 2007; Norra et al, 2004; Flynn et al,
2008). Abnormalities in lower frequency oscillations are
even more prominent. Augmented low-frequency oscilla-
tions are observed in unmedicated, first-episode, and
chronic schizophrenia patients (Clementz et al, 1994; Omori
et al, 1995; Karson et al, 1988; Sponheim et al, 1994, 2003;
Knott et al, 2001). A failure to suppress theta-alpha (B5 to
11Hz) activities during sensory gating significantly marks
the genetic liability for schizophrenia (Hong et al, 2008b).
However, negative and opposite findings also exist (Nagase
et al, 1992; Tauscher et al, 1998; Keshavan et al, 1998). The
underlying mechanisms of these abnormal clinical findings
at gamma and low frequencies remain elusive.
On the other hand, mechanisms of neural oscillations
have been extensively studied in animals. Neural oscilla-
tions may provide a needed bioassay platform for elucidat-
ing cellular and molecular mechanisms of human brain
diseases, especially when we can draw parallels between
oscillatory patterns of humans and animals. Scalp-recorded
electrical oscillations are considered to be summations of
local field potentials that are primarily originating from
excitatory and inhibitory postsynaptic potentials, while the
latter is closely related to glutamatergic and GABAergic
mechanisms, respectively. It is therefore not surprising that
Received 17 August 2009; revised 16 September 2009; accepted 17
*Correspondence: Dr Hong, Department of Psychiatry, Maryland
Psychiatric Research Center, University of Maryland School of
Medicine, PO Box 21247, Baltimore, MD 21228, USA, Tel: +1 410
402 6828, Fax: +1 410 402 6023, E-mail: firstname.lastname@example.org
Neuropsychopharmacology (2010) 35, 632–640
& 2010 Nature Publishing GroupAll rights reserved 0893-133X/10 $32.00
current theories on neural oscillations focus on glutama-
tergic (Cunningham et al, 2006; Roopun et al, 2008) and
GABAergic mechanisms (Whittington et al, 1995; Traub
et al, 1999; Gonzalez-Burgos and Lewis, 2008).
This study focuses on the glutamatergic effects on
oscillations in humans. Ketamine is a noncompetitive
N-methyl-D-aspartate (NMDA) receptor antagonist. In
subanesthetic dose, it mimics some features of schizo-
phrenia and is often used as a pharmacological model for
the NMDA hypofunction (Lahti and Tamminga, 1999;
Heresco-Levy and Javitt, 1998; Meador-Woodruff and
Healy, 2000; Coyle, 1996; Krystal et al, 1999). We examined
ketamine-induced oscillatory responses during auditory
paired-click sensory gating in normal controls using a
discrete wavelet method and compared these ketamine-
induced oscillatory responses to those seen in schizophrenia
patients (Hong et al, 2008b). The wavelet method was
believed to be sensitive for extracting single-trial-based
oscillatory activities (Hong et al, 2008a).
Most previous studies of ketamine effects on sensory
gating have found no significant effect of ketamine based on
averaged evoked potentials (AEPs) in humans (van Berckel
et al, 1998; Oranje et al, 2002) or animals (de Bruin et al,
1999). However, ketamine has shown to impair gating of
responses to repeated clicks presented at 100ms intervals
(Boeijinga et al, 2007) and reduce AEP response to the first
click in rodents (Amann et al, 2009). The endogenous
NMDA antagonist kynurenic acid also disrupts AEP N40
sensory gating (Shepard et al, 2003).
AEP-based sensory gating findings in schizophrenia are
somewhat mixed, with most studies showing a deficit
(Siegel et al, 1984; Clementz et al, 1998; Myles-Worsley,
2002; Sanchez-Morla et al, 2008), while some are negative
(Kathmann and Engel, 1990; Arnfred et al, 2003; de Wilde
et al, 2007). Gating of evoked oscillations in theta-alpha
range could have more robust group differences (Hong
et al, 2008b; Brockhaus-Dumke et al, 2008). Therefore, our
study focused on the effect of ketamine on single-trial-based
oscillatory responses. We aimed to determine whether the
effect of ketamine on neural oscillations in healthy humans
resembles oscillations observed in NMDA animal models
and oscillatory problems associated with schizophrenia
patients, potentially providing a glutamatergic account on
the part of the oscillatory problems found in schizophrenia
patients that may index glutamatergic abnormalities.
PATIENTS AND METHODS
A total of 10 healthy subjects (aged 34.5±11.7 years, 4
females) participated in a double-blind, placebo-controlled,
crossover study of bonus intravenous 0.3mg/kg ketamine
or saline placebo while double-click testing was adminis-
tered. Subjects were 21–55 years of age, with no history of
psychiatric illnesses. All subjects gave written informed
consent in accordance with the University of Maryland
Institutional Review Board guidelines. Subjects received
pretreatment double-click event-related potential (ERP)
testing, and then received ketamine (or placebo) in a
post-treatment ERP testing. This was repeated after 1 week
with the placebo (or ketamine) administration. The drug
order was considered not randomized in this study because
of a miscommunication with nursing staff for the order of
intravenous administration in a few cases, where the
randomization table was not followed (six received placebo
first and four ketamine first); although all clinical ratings by
a psychiatrist were given double-blinded to the drug
conditions. All data were processed under the blinded
condition. Clinical interviews included the 20-item Brief
Psychiatric Rating Scale (BPRS) (Hedlund and Vieweg,
1980) and the Clinician-Administered Dissociative States
Scale (CADSS) (Bremner et al, 1998) immediately after each
ERP testing. Following previous ketamine studies (Malhotra
et al, 1996), we calculated the BPRS total score and four
empirically derived factors: thought disorder, withdrawal–
retardation, hostility, and depressive–anxious symptom
factors. The CADSS has 27 items, each scored from 0 to 4,
including 19 subject-rated items (‘subjective symptoms’)
(Bremner et al, 1998).
EEGs were recorded in a sound-attenuated chamber, using a
cap containing six midline tin electrodes (Electro-Cap
International, Eaton, OH) referenced to linked earlobes.
Subjects were asked to relax with eyes open and listen to 150
pairs of click stimuli (1ms duration, 75dB amplitude,
500ms interclick interval, and 10s intertrial interval)
delivered through a pair of loudspeakers positioned 50cm
away from their ears. Electrooculograms were recorded
around the left eye. EEGs were sampled at 1kHz using a
NeuroScan Synamp amplifier (Neurosoft, El Paso, TX;
200Hz low pass, 1.0Hz high pass) to yield 500ms epochs,
including a 100-ms prestimulus window. Movement arti-
facts were removed from single trials with a rejection
criterion of±75mV followed by visual inspection. Electrode
impedance was maintained below 5kO. The central channel
(CZ) was used for the P50 (Nagamoto et al, 1989; Freedman
et al, 1997; Clementz et al, 1998). Data from all six channels
were used for oscillatory measures.
ERP Data Processing
The single-trial records were baseline-corrected, 3–100Hz
(24 octave slopes) bandpass filtered, and averaged to obtain
P50 waves. P50 response to the first stimulus (S1) was
defined as the largest positive-going wave occurring
35–75ms after the stimulus, measured from the trough of
the preceding wave to the P50 peak. The S2 P50 was set to
±10ms of the latency to S1 P50 (Nagamoto et al, 1989). P50
gating was the S2/S1 P50 ratio. P50 was scored blind to drug
For oscillatory measures, artifact-free single trials filtered
at 0.5–100Hz were used. A discrete wavelet transform
(DWT) approach was used. Wavelet transform of single-
trial recording has the advantage of not being biased by
trial-to-trial temporal variability because it extracts both
stationary and nonstationary energy. There is no reason to
believe that most biological or pharmacological actions use
only a stationary (time-locked) mechanism. Therefore,
single-trial-based analysis, by means of extracting both
Ketamine on neural oscillations
LE Hong et al
stationary and nonstationary energy, may be advantageous.
The theory and methodology of using DWT to decompose
evoked potential data have been examined (Hong et al,
2008a). We used an eight-level discrete biorthogonal
wavelet (bio5.5, Wavelet Toolbox; MathWorks Natick,
MA) to separate evoked responses into eight details
(D1–D8), which represent eight frequency bands. By
simulation, we estimated the frequency band of each detail:
D1–D3 corresponded to very fast gamma frequency
activities 485Hz; D4: 40–85Hz; D5: 20–40Hz; D6: 12–
20Hz; D7: 5–12Hz; and D8: 1–5Hz (more details of the
methodology are described in Hong et al, 2008a).
In previous studies using 125ms epochs, we found that
gating and schizophrenia patient–control differences on
theta-alpha band responses are largely limited to the 25–
275ms post-stimulus interval (Hong et al, 2008a,b).
Therefore, we elected to use a 300-ms post-stimulus epoch
for oscillatory response extraction in this study. The longer
epoch of 300ms (as compared with the previous 125ms
epoch) also allowed better resolution for extracting lower
frequency oscillations, especially at D8 (B1 to 5Hz). Energy
within each epoch of each frequency band (detail) was
measured by power spectrum density (PSD) using the
nonparametric Welch method (Welch, 1967; Kay, 1988).
Sensory gating of each time-frequency component was
calculated as the S2 PSD/S1 PSD ratio, and averaged across
all trials for each participant.
A four-way repeated-measures ANOVA was used to
compare drug (placebo vs ketamine), time (pre- and
postdrug), site (FZ, FCZ, CZ, CPZ, PZ, OZ), and frequency
post-hoc tests. Greenhouse–Geisser corrections were applied
to adjust for non-sphericity. The primary interests were
effects of ketamine; therefore, post-hoc tests were carried
corresponding followed by
out when there were significant interactions involving a
time?drug interaction. Significant time?drug findings on
frequency and/or site were followed by post-hoc paired
t-tests to determine whether differences between ketamine
and placebo were significant at the indicated frequency and/
or site. Potential effect of drug administration order was
explored by examining the effect of order on time, site, and
frequency. To account for potential differences at baseline
(pre- and postdrug order effect), post-hoc testing also
minus pre-ketamine) vs (post-placebo minus pre-placebo)).
Nominal significance (po0.05) and Bonferroni corrected
significance after correcting for eight detail levels (po0.05/8
¼0.00625) were reported. This procedure was carried out
for oscillatory response to the initial auditory sensory
stimulus (S1 only) and sensory gating (S2/S1 ratio)
separately. Oscillatory response to S2 was not separately
evaluated but rather evaluated in the context of the gating
ratio. AEP P50 and clinical measures were compared using
two-way repeated-measures ANOVA to examine significant
drug?time effects, followed by corresponding post-hoc
paired t-tests. All tests were two-tailed. Relationships
between clinical and electrophysiological measures signifi-
cantly affected by ketamine were examined using either
bivariate Pearson’s correlations or regression analyses if
more than one predictor was involved.
Compared with baseline and placebo, ketamine significantly
increased BPRS total scores and thought disorder and
withdrawal symptom factors (significant drug?time inter-
action), with the largest effect size on the withdrawal
symptoms (Table 1). For CADSS, ketamine significantly
increased the total score and the subjective symptom
Table 1 Effect of Subanesthetic Ketamine on Clinical Symptoms
Placebo KetamineDrug?time interaction
Thought disorder factor 0.0±0.0
Withdrawal–retardation factor 12.00.007*
Depressive–anxious symptoms factor 4.30.07
Objective symptoms2.9 0.12
Total 15.2 0.004*
BPRS, Brief Psychiatric Rating Scale; CADSS, Clinician-Administered Dissociative States Scale.
Statistics were drug?time interactions. For BPRS, a score of 1 (no symptom) on an item was converted to 0, so that sums of symptom severities in factors with
different number of items are comparable.
Ketamine on neural oscillations
LE Hong et al
subscore, but not the objective symptom subscore, com-
pared with baseline and placebo.
P50 Amplitude and Gating
There was no significant drug?time interaction on P50
amplitudes (Figure 1). Ketamine did not significantly affect
P50 gating (Figure 1, right panel; drug?time interaction
(F(1, 9)¼1.97, p¼0.19)). Exploratory comparison showed
that post-drug ketamine reduced gating compared with
post-drug placebo, although this was not statistically
significant (t¼2.15, p¼0.06).
Oscillatory Responses to Sensory Stimulus (S1 Single
There were significant three-way time?drug?frequency
(F(2.4, 19.3)¼4.35, p¼0.02), drug?frequency (F(1.5,
te?frequency (F(2.1, 17.1)¼3.17, p¼0.06) and site (F(5,
40)¼2.12, p¼0.08) effects. Post-hoc tests showed signifi-
cant drug (post-placebo vs post-ketamine)?frequency
interactions at the CZ (F(7, 18.1)¼4.87, p¼0.04)) and
weak evidence of drug?frequency effects at the FCZ
(p¼0.08) and FZ (p¼0.06), but not at the posterior sites
(all p40.21 in the CPZ, PZ, and OZ sites). Subsequent post-
hoc tests focused on the CZ, although most findings from
the FZ were similar (data not shown). Ketamine had an
opposite effect on gamma and low-frequency bands at the
CZ: ketamine increased gamma PSD (D4 or 40–85Hz:
t¼3.6, p¼0.006) and reduced low-frequency PSD (D7 or
theta-alpha at 5–12Hz: t¼?2.4, p¼0.04; D8 or delta at
1–5Hz: t¼?6.8, po0.001) compared with placebo during
post-drug testing (Figure 2, top). The results were similar in
post-drug minus pre-drug differences (ie, adjusted for
baseline) analyses: ketamine increased gamma (D4: t¼3.5,
p¼0.006) and reduced low-frequency power compared with
placebo (D7: t¼?2.7, p¼0.02; D8: t¼?3.7, p¼0.005)
(Figure 2, bottom). The effects on D4 (gamma) and D8
(delta), but not on D7 (theta-alpha), were significant after
Bonferroni corrections. The D4 and D8 responses of each
subject were plotted in Figure 3. There were no significant
time?drug interactions on frequency or site when
oscillatory responses to S2 were examined (data not shown).
Finally, there were no significant effects of drug order on
frequency or site.
To summarize, ketamine significantly increased gamma
and reduced low-frequency oscillations in response to
p¼0.02), and weakevidenceof si-
auditory sensory stimuli, an effect most prominently shown
at the CZ site.
Gating of Oscillatory Responses (S2/S1 Ratio)
There were significant three-way time?drug?frequency
(F(3.2, 29.2)¼2.29, p¼0.038), two-way drug?frequency
(F(3.4, 30.8)¼4.60, p¼0.007), time?frequency (F(2.4,
35.6)¼3.36, p¼0.02) interactions. The three-way time-
drug?frequency interaction was significant only at the CZ
(F(3.5, 31.9)¼2.84, p¼0.046) and with weak evidence for
interaction at the neighboring CPZ (p¼0.09) and FCZ
(p¼0.07). We again used only the CZ for post-hoc analyses.
Ketamine increased the S2/S1 ratio (reduced gating) at D7
(t¼4.97, p¼0.001) and D8 (t¼2.65, p¼0.03) compared
with placebo during post-drug testing (Figure 4 top). The
p¼0.03), andsite?frequency (F(4.0,
ratio. There was no significant drug?time interaction in any of the
Effects of subanesthetic ketamine on P50 amplitudes and P50
oscillatory responses to single auditory click (S1) at the CZ. Top: comparing
the ketamine effect based on post-drug recordings, ie, post-placebo vs
post-ketamine; bottom: comparing the ketamine effect after adjusting for
baseline recordings. The corresponding frequency bands of the discrete
wavelet details: D4: 40–85Hz (gamma); D5: 20–40Hz (low gamma); D6:
12–20Hz (beta); D7: 5–12Hz (theta-alpha); and D8: 1–5Hz (delta). D1–
D3 correspond to very fast activities 485Hz (the precise range of
frequencies for detail level D1 and D2 has not been formally simulated
except that they represent very high and ultra high gamma. Note that
frequency band 4100Hz was filtered out in this experiment). *Nominally
significant po0.05. **Significant after Bonferroni correction.
Post-hoc paired t-tests of subanesthetic ketamine effect on
Ketamine on neural oscillations
LE Hong et al
results were similar after adjusting for baseline: ketamine
increased the ratio at D7 (t¼3.84, p¼0.004) and D8
(t¼2.68, p¼0.02) compared with placebo (Figure 4 bot-
tom). The effect on D7 gating was significant after the
Electrophysiology – Clinical Rating Correlates
To limit multiple comparisons, only measures significantly
affected by ketamine were examined in their relationships
with clinical ratings. Only clinical and electrophysiological
data recorded during the ketamine infusion session were
used because participants essentially have no clinical
symptoms during the other three sessions (ie, little or no
clinical variance, see Table 1).
To examine potential contributions of S1 oscillatory
responses at D4 (gamma at CZ) and D8 (delta at CZ) and
their interaction to clinical ratings, a regression model was
used, in which the dependent measure was a clinical rating
and the predictors were D4, D8, and their interaction term.
For BPRS total and factor scores, the only significant model
was with the withdrawal symptom factor (model R2
change¼79.3%, F(3,9)¼7.7, p¼0.018). The D4?D8 inter-
action term was significant (standardized beta¼?5.7,
t¼?3.1, p¼0.02). Partial correlation suggested that the
severity of withdrawal symptoms experienced by the
subjects were positively correlated with D4 and negatively
correlated with D8 (Figure 5). This finding is interesting
because withdrawal symptoms also showed the most robust
ketamine effect (Table 1) and a D4?D8 interaction
explained a large proportion of the withdrawal–retardation
experience. We failed to find significant regression models
that may explain the CADSS total or subscale scores.
To examine the effect of D7 gating on clinical symptoms,
Pearson’s correlations were carried out between the D7 S2/
S1 ratio and clinical ratings. No significant correlations
were found for BPRS or CADSS.
We found that subanesthetic dose of ketamine has opposite
effects on human gamma and low-frequency oscillations:
ketamine augmented gamma (40–85Hz) and reduced delta
(1–5Hz) and, to some extent, theta-alpha (5–12Hz)
frequency oscillations compared with placebo. This finding
replicated similar protocols in mouse (Ehrlichman et al,
2009; Lazarewicz et al, 2009) and rats (Hunt et al, 2009), in
which ketamine increased gamma and reduced low-
frequency oscillations, although the clinical correlates of
these oscillatory effects were not clear from earlier animal
studies. Subanesthetic ketamine is known to reliably
increase thought disorder and withdrawal–retardation
symptoms as measured by corresponding BPRS factors in
under placebo (Pla) vs subanesthetic ketamine (Ket). Most subjects showed
a pattern of increased gamma and reduced delta. Data were adjusted for
pre-drug baseline. One subject showed an unusually large order effect on
gamma (the subject on the top of the left panel), with increased gamma in
post-drug compared with pre-drug condition. Although this order effect
was clearly an outlier compared with the rest of the subjects, this order
effect was present in both placebo and ketamine sessions and the drug
effects (ketamine minus placebo) on both gamma and delta bands were
not considered outliers.
Plotting individual subject’s oscillatory response to single click
oscillatory responses (S2/S1 ratio) at the CZ. Top: comparing the ketamine
effect based on post-drug recordings, ie, post-placebo vs post-ketamine.
Increases in the ratio imply worse sensory gating and vice versa. The
ratio¼1 if no gating is present. Bottom: comparing the ketamine effect after
adjusting for baseline recordings. Here, value¼0 if there is no change in
gating between pre-drug (baseline) and post-drug. Increases in the ratio
imply worse sensory gating and vise versa. *Nominally significant po0.05.
**Significant after Bonferroni correction.
Post-hoc paired t-tests of the ketamine effect on gating of
Ketamine on neural oscillations
LE Hong et al
healthy controls (Krystal et al, 1994; Malhotra et al, 1996;
Newcomer et al, 1999; Lahti et al, 2001) and worsen these
symptoms in schizophrenia patients (Lahti et al, 1995, 2001;
Malhotra et al, 1997), whereas its effects on BPRS hostility
and mood symptoms were usually statistically insignificant
(Malhotra et al, 1997; Lahti et al, 2001). This clinical profile
was replicated in this sample (Table 1). Importantly, the
withdrawal symptoms were significantly predicted by the
gamma and low-frequency interaction: the withdrawal
experience was positively correlated with D4 or gamma
and negatively correlated with D8 or delta power during
The following discussion focuses on whether the keta-
mine effect on oscillatory pattern mimics those observed in
NMDA animal studies and abnormal oscillations in schizo-
Increased gamma power by ketamine is a consistent
finding in human (Plourde et al, 1997) and in vivo
(anesthetized or awake) animal studies (Hunt et al, 2006;
studies initially described reduced gamma by ketamine
(Cunningham et al, 2006; Zhang et al, 2008), although
subsequent studies have refined this description by
suggesting a regionally specific ketamine effect such that
ketamine increases gamma at the primary auditory cortex
but reduces or has no effect in other brain regions (Roopun
et al, 2008). Therefore, the observed gamma augmentation
at D4 (40–85Hz) is consistent with prior observations.
NMDA receptor antagonists increase the spontaneous firing
rate of neurons in the medial PFC and nucleus accumbens
(NAc) (Jackson et al, 2004; Hunt et al, 2008). Whether the
effect on increased gamma observed in the human scalp is
related to the corresponding rodent anatomical locations
require further studies. We did not find significant
ketamine effects on the low range of gamma (20–40Hz).
One study reported that general anesthetic dose (2mg/kg)
ketamine did not change 30–40Hz power in response to
auditory click in humans (Schwender et al, 1993).
Whether the increased gamma by ketamine is mimicking
schizophrenia is less clear. As discussed in the Introduction,
although most studies have shown a gamma band reduction
in schizophrenia, increased gamma band in schizophrenia
has also been shown. Previous data also showed that gamma
band is positively correlated to the severity of psychosis
(Spencer et al, 2004). Our finding suggests that ketamine-
induced gamma augmentation, or rather a ‘theta-to-gamma
et al, 2009). In vitro
shift’ (Ehrlichman et al, 2009), is positively correlated to the
severity of withdrawal symptoms. However, it should be
cautioned that the psychotomimetic withdrawal–retardation
symptoms reported under acute ketamine may not be
equivalent to negative symptoms in schizophrenia, because
these symptoms occur while the person may be simply more
occupied by internal subjective experience rather than a
true avolitional pathology.
Although ketamine induces NMDA receptor hypofunc-
tion, acute ketamine leads to increased glutamate in the
extracellular space in animals (Moghaddam et al, 1997) and
increased glutamate in humans as measured by magnetic
resonance spectroscopy (Rowland et al, 2005). Glutamate
level is reduced in chronic schizophrenia (Theberge et al,
2003; Ohrmann et al, 2005, 2007) but increased in early
psychosis (Theberge et al, 2002; Bartha et al, 1997). In
parallel, gamma is often reported to be reduced in chronic
schizophrenia, although increased gamma is found in early
psychosis (Flynn et al, 2008). NMDA receptor blockade may
cause hyperexcitation of glutamatergic neurons by disin-
hibition of GABAergic interneurons (Homayoun and
Moghaddam, 2007). Perhaps, a mild shift in the excita-
tion/inhibition balance toward excitation could result in
gamma augmentation observed under subanesthetic keta-
mine. Taken together, we speculate that the augmented
gamma indexes an increased glutamate secondary to NMDA
antagonism. Further animal and clinical studies are
required to investigate this possibility.
The finding of ketamine-induced theta-alpha and delta
reduction is also consistent with the animal literature:
NMDA and glutamate infusions augment hippocampal
delta, theta, and alpha oscillations (Bonansco and Buno,
2003; Bland et al, 2007; Carre and Harley, 2000), whereas
NMDA antagonists attenuate the augmentation effect
(Bland et al, 2007). Delta-range oscillations are shown to
correlate with the NAc neuron membrane potential changes
(O’Donnell and Grace, 1995). NMDA antagonist reduces
NAc delta power (Hunt et al, 2009) and induces glutamate
release in NAc (Razoux et al, 2007). It has been suggested
that this ketamine effect in NAc may mediate a psychotic-
like effect of ketamine through the potentiation of synaptic
efficacy in the prefrontal cortex–accumbens pathway
(Razoux et al, 2007). It would be interesting to investigate
whether this mechanism could explain a correlation
between reduced delta and more ketamine-induced with-
drawal symptoms. Importantly, the seemingly analogous
partial correlation graphs. Scales in graphs are residuals of the dependent variable (withdrawal symptoms) and the predictors (gamma, delta). The
gamma?delta interaction term was significant (p¼0.020).
Contributions of gamma (D4, gamma) and low-frequency (D8, delta) interaction on BPRS withdrawal–retardation symptoms as shown by
Ketamine on neural oscillations
LE Hong et al
pattern of ketamine effects on gamma and low frequencies
between rodents and humans is encouraging because it
provides an opportunity to study the NMDA mechanisms in
schizophrenia in translational preclinical–clinical studies
using oscillations as intermediate bioassays.
Subanesthetic ketamine appears to mimic some but not
all aspects of sensory gating deficits in schizophrenia.
Ketamine significantly reduced single-trial-based theta-
alpha gating. This replicated the same finding in schizo-
phrenia patients under essentially the same data processing
methods (Hong et al, 2008b), which showed that patients
and their non-ill first-degree relatives have significantly
reduced theta-alpha gating while the reduction of P50 gating
was not significant. The question of whether sensory gating
is under the control of NMDA receptors has been evaluated.
The lack of ketamine effect on AEP-based P50 is a
replication of previous reports, all of which showed that
ketamine itself did not significantly affect P50 gating in
humans (van Berckel et al, 1998; Oranje et al, 2002). Animal
studies suggest that ketamine has either no effect (de Bruin
et al, 1999) or even increase in P20/N40 (the rodent AEP
component believed to be equivalent to human P50)
(Connolly et al, 2004). The negative findings can be due
to a lack of significant NMDA receptor involvement in
sensory gating, insufficient dose of ketamine, or methodol-
ogy issues. We observed much similarity between ketamine
and schizophrenia on gating of theta-alpha oscillation,
indicating that the negative AEP-based findings may in part
be due to insensitive methodology.
This observation would suggest that sensory gating
problems observed in schizophrenia might in part be
associated with a NMDA receptor deficit or antagonism.
However, we also observed one distinct difference: the
reduced theta-alpha gating in schizophrenia patients was
observed on the background of elevated theta-alpha
oscillations compared with healthy controls (Hong et al,
2008b; Figure 2). Here, we observed an opposite effect such
that ketamine reduced low-frequency oscillations. The
reason for this contradiction is not known and may seem
at odd with the finding of similar theta-alpha gating deficit
between schizophrenia and ketamine challenge. However,
acute ketamine does not necessarily mimic all aspects of
schizophrenia. Acute ketamine in similar dose and study
design has also generated an opposite effect on prepulse
inhibition compared with that in schizophrenia, by
increasing rather than decreasing prepulse inhibition (Abel
et al, 2003).
To summarize, glutamatergic receptors are integral
components of current theories on rhythmatogenesis
(Cunningham et al, 2006; Gonzalez-Burgos and Lewis,
2008; Roopun et al, 2008) and may also be associated with
abnormal electrical brain oscillations in schizophrenia. We
observed that acute ketamine affects both gamma and low-
frequency bands in humans, which mimic some aspects of
the oscillatory pattern observed in schizophrenia. Our
finding also suggests that the ketamine-induced gamma/
delta reversal seen in animal studies (Ehrlichman et al,
2009) maybe indexing some aspects of schizophrenia-like
symptoms. Given the small sample size, our study might
have missed some other important electrophysiology–
clinical correlates. Overall, this study illustrates the
potentials of using frequency-based neural oscillations to
complement and, in some cases, to replace AEP for animal-
clinical translational studies.
The study was supported by the National Institute on
Health grants MH70644, 79172, 49826, 77852, and 68580.
The authors declare no conflict of interest.
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