Article

Gamma and Delta Neural Oscillations and Association with Clinical Symptoms under Subanesthetic Ketamine

Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD 21228, USA.
Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology (Impact Factor: 7.83). 11/2009; 35(3):632-40. DOI: 10.1038/npp.2009.168
Source: PubMed

ABSTRACT 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 40-85 Hz, p=0.006) and reduced low-frequency oscillations (delta band at 1-5 Hz, p<0.001) 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 (p=0.02). Ketamine also reduced gating of the theta-alpha (5-12 Hz) 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.

2 Followers
 · 
132 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Large 'real world' studies demonstrating the limited effectiveness and slow onset of clinical response associated with our existing antidepressant medications has highlighted need for the development of new therapeutic strategies for major depression and other mood disorders. Yet, despite intense research efforts, the field has had little success in developing antidepressant treatments with fundamentally novel mechanisms of action over the past six decades, leaving the field wary and skeptical about any new developments. However, a series of relatively small proof of concept studies conducted over the last 15 years has gradually gained great interest by providing strong evidence that a unique, rapid onset of sustained, but still temporally limited, antidepressant effects can be achieved with a single administration of ketamine. We are now left with several questions regarding the true clinical meaningfulness of the findings and the mechanisms underlying the antidepressant action. In this Circumspectives piece, Dr. Sanacora and Schatzberg share their opinions on these issues and discuss paths to move the field forward.Neuropsychopharmacology accepted article preview online, 26 September 2014; doi:10.1038/npp.2014.261.
    Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 09/2014; 40(5). DOI:10.1038/npp.2014.261 · 7.83 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Impairments in spatial and temporal integration of brain network activity are a core feature of schizophrenia. Neural network oscillatory activity is considered to be fundamentally important in coordinating neural activity throughout the brain. Hence, exploration of brain oscillations has become an indispensible tool to study the neural basis of mental illnesses. However, most of the studies in schizophrenia include medicated patients. This implicates the question to what extent are changes in the electrophysiological parameters genuine illness effects, genuine drug effects or a mixture of both. We here provide a short overview of the neuropharmacology of brain oscillations with respect to schizophrenia.
    International Journal of Psychophysiology 02/2015; DOI:10.1016/j.ijpsycho.2015.02.014 · 2.65 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Conventional analysis of clinical resting electroencephalography (EEG) recordings typically involves assessment of spectral power in pre-defined frequency bands at specific electrodes. EEG is a potentially useful technique in drug development for measuring the pharmacodynamic (PD) effects of a centrally acting compound and hence to assess the likelihood of success of a novel drug based on pharmacokinetic?pharmacodynamic (PK?PD) principles. However, the need to define the electrodes and spectral bands to be analysed a priori is limiting where the nature of the drug-induced EEG effects is initially not known. We describe the extension to human EEG data of a generalised semi-linear canonical correlation analysis (GSLCCA), developed for small animal data. GSLCCA uses data from the whole spectrum, the entire recording duration and multiple electrodes. It provides interpretable information on the mechanism of drug action and a PD measure suitable for use in PK?PD modelling. Data from a study with low (analgesic) doses of the ?-opioid agonist, remifentanil, in 12 healthy subjects were analysed using conventional spectral edge analysis and GSLCCA. At this low dose, the conventional analysis was unsuccessful but plausible results consistent with previous observations were obtained using GSLCCA, confirming that GSLCCA can be successfully applied to clinical EEG data.
    Physiological Measurement 11/2014; 35(12):2459. DOI:10.1088/0967-3334/35/12/2459 · 1.62 Impact Factor

Full-text (2 Sources)

Download
6 Downloads
Available from
Mar 19, 2015