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Original Investigation |

Frontal Glutamate and γ-Aminobutyric Acid Levels and Their Associations With Mismatch Negativity and Digit Sequencing Task Performance in Schizophrenia

Laura M. Rowland, PhD1,2,3,4; Ann Summerfelt, MA1; S. Andrea Wijtenburg, PhD1; Xiaoming Du, PhD1; Joshua J. Chiappelli, MD1; Nithin Krishna, MD1; Jeffrey West, MA1; Florian Muellerklein, BA1; Peter Kochunov, PhD1,3,4; L. Elliot Hong, MD1
[+] Author Affiliations
1Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
2Department of Radiology and Radiological Sciences, the Johns Hopkins University School of Medicine, Baltimore, Maryland
3Department of Psychology, University of Maryland, Baltimore County, Baltimore
4Department of Physics, University of Maryland, Baltimore County, Baltimore, Maryland
JAMA Psychiatry. 2016;73(2):166-174. doi:10.1001/jamapsychiatry.2015.2680.
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Importance  Auditory mismatch negativity (MMN) is a biomarker for schizophrenia thought to reflect glutamatergic N-methyl-d-aspartate receptor function and excitatory-inhibitory neurotransmission balance. However, the association of glutamate level with MMN has not been directly examined in patients with schizophrenia, to our knowledge.

Objective  To investigate the contributions of glutamate and γ-aminobutyric acid (GABA) to MMN and digit sequencing task (DST) performance, an assessment of verbal working memory, in schizophrenia.

Design, Setting, and Participants  Fifty-three control participants from the community and 45 persons with schizophrenia from outpatient clinics completed an electroencephalographic session for MMN, magnetic resonance spectroscopy for glutamate and GABA, and a DST. The study dates were July 2011 to May 2014, and the dates of our analysis were May 2014 to August 2015.

Main Outcomes and Measures  Glutamate, GABA, the ratio of glutamine to glutamate, MMN amplitude, and DST. Structural equation modeling was used to test the effects of neurochemistry and MMN amplitude on DST performance.

Results  The 45 persons with schizophrenia were a mean (SD) of 37.7 (12.8) years and the control participants were 37.1 (13.1) years. The schizophrenia group had a mean (SD) of 14.7 (12.1) years of illness. Mismatch negativity amplitude (F = 4.39, P = .04) and glutamate (F = 9.69, P = .002) were reduced in the schizophrenia group. Smaller MMN amplitude was significantly associated with lower GABA level (P = .008), lower glutamate level (P = .05), and higher ratio of glutamine to glutamate (P = .003). Reduced MMN amplitude was linked to poor verbal working memory in schizophrenia (P = .002). Modeling revealed that a proxy of glutamatergic function, indexed by the ratio of glutamine to glutamate, influenced a path from the ratio of glutamine to glutamate to MMN to verbal working memory (P = .38 [root-mean-square error of approximation, P < .001] by χ2 test), supporting the contention that MMN serves as an intermediate biomarker linking glutamatergic function to DST performance in schizophrenia.

Conclusions and Relevance  The role of glutamate and GABA in MMN and verbal working memory deficits in schizophrenia has been frequently debated. These data provide in vivo evidence that support glutamatergic and GABAergic regulation of MMN and verbal working memory function in schizophrenia.

Figures in this Article


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Figure 1.
Voxel Location, Representative Spectra, and Mismatch Negativity (MMN) Waveforms

A, Shown is the medial frontal and anterior cingulate voxel (white rectangle). The lines indicate the center of the voxel. B, GABA indicates γ-aminobutyric acid. C, Spectra (gray line) and spectral fit (red line) for glutamine and glutamate acquisition are shown. Residual is seen at the top. Cho indicates choline; Cr, creatine; Glu, glutamate; Gln, glutamine; NAA, N-acetylaspartate; mI, myo-inositol; and mm, macromolecule. D, The total mean values are shown.

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Figure 2.
Correlations Between Mismatch Negativity (MMN) and Ratio of Glutamine to Glutamate, γ-Aminobutyric Acid (GABA), and Glutamate
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Figure 3.
Structural Equation Modeling of γ-Aminobutyric Acid (GABA) and Glutamine and Glutamate Contributions to Digit Sequencing Task (DST) Performance Through Mismatch Negativity (MMN) Mediation

A, Mismatch negativity was a significant mediator of glutamine and glutamate contribution to DST performance. B, Although both GABA and glutamine and glutamate contributed to MMN, they were not related to DST performance.

aThe letter a and the thick lines indicate statistical significance.

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Letter to the Editor: “Frontal Glutamate and γ-Aminobutyric Acid Levels and Their Associations with Mismatch Negativity and Digit Sequencing Task Performance in Schizophrenia” JAMA Psychiatry.2015 Dec
Posted on February 16, 2016
Jianbo Liu#,Xilong Cui#, Xuerong Luo* #These authors contributed equally to this work. *Corresponding author: Xuerong Luo
Mental Health Institute of The Second Xiangya Hospital and Key Laboratory of Psychiatry and Mental Health of Hunan Province, The Central South University, Changsha 410000, China
Conflict of Interest: None Declared

In their study, Laura M.Rowland et al (1) used structural equation modeling (SEM) to test the effects of neurochemistry and auditory mismatch negativity (MMN) amplitude on digit sequencing task (DST) performance, and found MMN served as an intermediate biomarker linking glutamatergic function to DST performance in schizophrenia. However, considering the potential statistic bias caused by small sample size, a relatively larger sample size was recommended for SEM. Schumacker and Lomax (2) believed that a sample size less than 100 would not be reliable in SEM. Obviously, the quantity of samples of this article (53 controls and 45 patients) was not enough. Besides, the authors didn’t report whether the variables were normally distributed in the SEM in advance, which is an important pre-requisite. Finally, the authors performed mediation by causal steps approach. Although causal steps approach is relatively simple and understandable, it was questioned and criticized due to its low statistic power. So most scholars recommended to use the bootstrapped percentile confidence interval to assess mediational model because it can maintain Type I error rates,exhibit reasonable statistic power (3).

It is found that cognition in schizophrenia declined with the evolution of the disease progressively (4). As a relatively long span of illness (14.7±12.1 years) was reported in this study, it is typically required to take the duration of illness into account in statistical analysis to make the results more reliable. Besides, the authors used the Brief Psychiatric Rating Scale (BPRS) to assess symptoms of schizophrenia patients. To our knowledge, there are several versions of the BPRS (such as 16-item BPRS, 18-item BPRS and 24- item BPRS) and the factor structures vary accordingly. But we don’t know which version was used in this study since the authors did not give the reference. Whichever version was used,relatively low BPRS scores of schizophrenia group were reported in this study. We believe that some of the patients recruited in this study were in remission stage without auditory hallucinations (AHs), and others were in a phase with psychotic symptoms, especially AHs. Existing evidence (5,6) showed that AHs made a significant contribution to MMN deficits in schizophrenia and reduced MMN amplitude was found in hallucinating patients while not in non-hallucinating patients compared with healthy controls. It is strongly recommended that the authors divide the patients into hallucinating group and non-hallucinating group and analysis the data in more detail. Thereby, it can help clarify whether the present finding is applicable to all the schizophrenia patients, or it is just restricted to the ones with AHs, which make the article more informative.

1.Rowland, L.M., Summerfelt,L.,Wijtenburg,A.,et al., Frontal Glutamate and
amma-Aminobutyric Acid Levels and Their Associations With Mismatch Negativity and Digit Sequencing Task Performance in Schizophrenia. JAMA Psychiatry. 2015; 1-10.
2.Schumacker,R.E.,Lomax,R.G., A beginner's guide to structural equation modeling,(2nd ed). Mahwah, NJ: Lawrence Erlbaum Associates, 2004.
3.Biesanz, J.C., Falk ,C.F., Savalei, V., Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects. Multivariate Behav Res.2010; 45(4): 661-701.
4. Napal, O., Ojeda, N., Sanchez, P., et al., The course of the schizophrenia and its impact on cognition: a review of literature. Actas espanolas de psiquiatria .2012;40, 198-220.
5. Fisher, D.J., Labelle, A., Knott, V.J., Alterations of mismatch negativity (MMN) in schizophrenia patients with auditory hallucinations experiencing acute exacerbation of illness. Schizophrenia research.2012;139, 237-245.
6. Fisher, D.J., Labelle, A., Knott, V.,J.,2008. The right profile: mismatch negativity in schizophrenia with and without auditory hallucinations as measured by a multi-feature paradigm. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 119, 909-921
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