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Glutamate as a mediating transmitter for auditory hallucinations in schizophrenia: A 1H MRS study

Schizophrenia Research, Volume 161, Issue 2-3, February 2015, Pages 252 - 260

Editorial Comment:
Glutamate is the predominant excitatory transmitter in the brain and has been implicated in the pathogenesis of schizophrenia. This study used 1H MR spectroscopy (MRS) to estimate glutamate levels in the temporal and frontal lobes of patients with schizophrenia and make comparisons to a healthy control group. The patient group had reduced glutamate levels compared to healthy controls which is consistent with prior research on glutamate regulation in schizophrenia. However, a novel finding was that within the patient group, glutamate levels were increased in those with more frequent and severe auditory hallucinations compared to those with less frequent and less severe hallucination. This suggests that glutamate may be involved in the neurochemistry of auditory hallucinations in schizophrenia

Prof. Peter Haddad, University of Manchester, UK

Abstract

This is a 1H MR spectroscopy (MRS) study of glutamate (Glu), measured as Glx, levels in temporal and frontal lobe regions in patients with schizophrenia compared with a healthy control group with the objective of revealing aspects of the underlying neurochemistry of auditory hallucinations. We further compared and correlated Glu(Glx) levels for the patients-only against frequency and severity of auditory hallucinations and the sum of Positive symptoms, and also for frequency and severity of emotional withdrawal, and sum of Negative symptoms. The sample included 23 patients with an ICD-10 and DSM-IV diagnosis of schizophrenia, and 26 healthy control subjects without any known psychiatric or neurological disorders. Symptom scores were obtained from the Positive and Negative Syndrome Scale (PANSS). 1H MRS data were acquired on a 3 T MR scanner from two temporal and two frontal voxels, using standard sequences and analysis parameters. The results showed that schizophrenia patients as a group had reduced Glu(Glx) levels in the voxels of interest compared to the healthy control subjects, while increased levels were found for patients with frequent and severe auditory hallucinations, relative to patients with less frequent and severe hallucination. We further found significant positive correlations between frequency and severity of auditory hallucinations, and for sum Positive symptoms, and Glu(Glx) levels in all regions, not seen when the analysis was done for negative symptoms. It is concluded that the results show for the first time that glutamate may be a mediating factor in auditory hallucinations in schizophrenia.

Keywords: MR, glutamate, MR spectroscopy, schizophrenia, auditory hallucinations.

1. Introduction

A common finding in current neuroscience research on auditory hallucinations in patients with schizophrenia is aberrant inner-speech perceptions with a neuronal locus in temporal areas (Silbersweig et al, 1995, McGuire et al, 1995, McGuire et al, 1996, Hugdahl et al, 2012, Kompus et al, 2011, Jardri et al, 2010, and Bentaleb et al, 2002, see also Aleman and Vercammen, 2012 and Allen and Modinos, 2012 for reviews), and maintained through dysfunctional inhibitory cognitive control (Løberg et al., 1999) which is a also key cognitive deficit in schizophrenia (Green et al, 2000 and Rund et al, 2006). It has been suggested that failure of inhibitory control of auditory hallucinations is mediated through reduced fronto-temporal connectivity and reduced cortical inhibitory control, seen in both electrophysiological and other imaging measures (Ford et al, 2001, Ford et al, 2002, Ford et al, 2012, Haraldsson et al, 2004, and Lewis et al, 2005).

What has not been frequently studied, nor understood, is the underlying neurochemistry of the brain abnormalities that have been found related to auditory hallucinations. Such an approach has been hampered by the lack of adequate methods to measure brain transmitters and metabolites in-vivo in the schizophrenia brain, with the exception of PET studies (see David, 1999 and Poels et al., 2014 for early and recent reviews, respectively), which however mainly have been focused on medication effects Thus, most of our knowledge of the neurochemistry in schizophrenia comes from animal models, post-mortem, and from brain imaging studies that have primarily been focused on the understanding of the underlying pharmacology of antipsychotic medication (e.g. Kapur et al, 2000 and Sanjuan et al, 2010, see also Kegeles et al, 2012, de la Fuente-Sandoval et al, 2011, de la Fuente-Sandoval et al, 2013a, de la Fuente-Sandoval et al, 2013b, Kraguljac et al, 2013, and Olney and Farber, 1995). Such models are however far from the phenomenology of the subjective experience of “hearing an external voice” which the patient often is convinced of is real and of personal significance despite objective evidence to the contrary. It is therefore important to study the hallucinating brain in-vivo, in order to reveal the underlying neurochemistry.

The re-emerging interest in MR spectroscopy (MRS) through the availability of higher field strengths, such as 3 T and above, has recently opened a new window into understanding the underlying neurochemistry related to psychiatric disorders (see Taylor, 2014, Stagg and Rothman, 2014, and Puts and edden, 2012 reviews). A common MRS technique for use in neuroscience research and with clinical applications in psychiatry and related disciplines is 1H MRS which is based on the chemical shift from the hydrogen molecule (1H) (see Stagg and Rothman, 2014 for overview). 1H MRS is used for quantitative measurements of relative levels of chemical compounds, i.e. metabolites, in the brain. The observable signals from these metabolites are localized spectrally between the water and lipid peaks in the MRS spectrum, but are much weaker than the signal from these latter compounds, which provide the basis for structural MR images. Examples of brain metabolites obtained from 1H MRS measurements are N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho), myoinositol (ml), glutamine (Gln), and others (Brandao and Domingues, 2004).

Of particular interest in relation to auditory hallucinations is the Glutamate (Glu)1 peak in the MRS spectrum, and whether Glu level is affected in schizophrenia patients compared with healthy controls, and especially if patients with frequent and severe auditory hallucinations differ from patients with infrequent and less severe hallucinations. Any attempt at development of new pharmacological drugs for alleviation of hallucinations and other symptoms must be based on an understanding of the neurochemistry of such symptom-specific abnormalities (see Sanjuan et al., 2010). Such a development will not come from a better understanding of cognitive deficits or from structural and functional imaging studies alone.

There are several studies on the relationship between cortical Glu levels in schizophrenia patients (see Marsman et al., 2013 for a recent meta-analysis), which essentially have revealed altered Glu levels in patients compared to control subjects (see however Poels et al., 2014). What is not clear is, however, if such alterations may be affected by antipsychotic medication, and whether increases or decreases should be expected in such cases. Several recent studies on first-episode and prodromal schizophrenia patients have found increased glutamate and glutamate + glutamine (Glx) levels that have been “normalized” after medication (de la Fuente-Sandoval et al, 2011, de la Fuente-Sandoval et al, 2013a, and de la Fuente-Sandoval et al, 2013b), and in otherwise unmedicated schizophrenia patients (Kegeles et al, 2012 and Kraguljac et al, 2013). The brain regions where Glu and Glx MRS data were acquired from varied however substantially between these studies, from sub-cortical to cerebellar to cortical areas, and in none of the studies did the authors look specifically for relationship between auditory hallucinations and medication effects. It is furthermore not clear how reduction of Glu could be a sign of “normalization” of Glu and Glx levels (Kegeles et al., 2012) after antipsychotic medication when other studies have found that glutamate levels are reduced in chronic schizophrenia patients (Carlsson et al, 2001, Coyle et al, 2012, and Johnsen et al, 2013).

Although there are several 1H MRS-studies comparing schizophrenia patients and healthy controls, there are to our knowledge no studies, nor any consistent knowledge regarding the specific relationship between 1H MRS Glu levels and auditory hallucinations in schizophrenia patients. In a recent overview and review of the field by Allen et al. (2012), the only MRS finding with regard to auditory hallucinations was for reduced NAA/Cho ratios in the thalamus, when reviewing data from Martinez-Granados et al. (2008), see also Nenadic et al. (2013).

Glu is the major excitatory transmitter in the brain and the most widespread transmitter in prefrontal and temporal regions, and since auditory hallucinations phenomenologically speaking are “excitatory” for the patient and localized to the same cortical areas. It is therefore not unreasonable to suggest that Glu levels actually may be increased in patients with frequent and severe auditory hallucinations, despite that schizophrenia patients in general have been found to show decreased Glu levels (see reviews by Coyle et al, 2012 and Carlsson et al, 2001). It would similarly not be unreasonable to suggest that such a relationship between Glu and auditory hallucinations might extend to other positive symptoms, considering that the hallucination symptom is highly correlated with other positive symptoms, but not with negative symptoms. For example, Wallwork et al. (2012) found that ‘hallucinations’, ‘delusions’, ‘grandiosity’, and ‘unusual thought content’, clustered together in a factor analysis of scores on the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987). Hallucination symptom scores in the present study were obtained from the PANSS interview questionnaire (Kay et al., 1987) and were compared and correlated with 1H MRS spectroscopy data. PANSS scores for Negative symptoms were used to control for the specificity of any association between Glu and auditory hallucinations.

2. Methods

2.1. Subjects

The subjects were 24 (19 males and 5 females) schizophrenia patients (Schz group), who had been diagnosed with the ICD-10 and DSM-IV diagnostic systems. With regards to antipsychotic medication, nine patients used olanzapine, two patients used risperidone, whereas aripiprazole, clozapine, zuclopentixol, and amisulpride, respectively, were used by one patient each. Three patients used combinations of two different antipsychotics. Two patients did not use any medication, and for three patients the medication details were not known. Antipsychotic drug doses were converted to chlorpromazine equivalent doses (Davis, 1974), and to defined daily doses (DDDs) as developed by the World Health Organization Collaborating Centre for Drug Statistics Methodology (Ronning, 1982). The basic definition of the DDD unit is the assumed average maintenance dose per day for a drug used for its main indication in adults. Mean chlorpromazine dosage equivalents was 307.59, SD 208. 53, with mean DDD of 1.27. Mean Global Assessment of Functioning (GAF) was 45.29, SD 16.71. Mean duration of illness was 12.25 years, SD 10.34. In order to rule out that eventual medication effects could have interfered with the results, we did a separate comparison with the patients who were unmedicated/unknown versus the medicated patients. The ANOVA showed no significant effect between the sub-groups, although the unmedicated/unknown sub-group had slightly higher mean Glx level than the medicated sub-group (mean 9.308, SE 1.75 vs. 8.603, SE 0.94, respectively). Thus, although not significant, the results were in the same direction as for previous studies comparing unmedicated patients (Kegeles et al, 2012, Kraguljac et al, 2013, de la Fuente-Sandoval et al, 2013a, and de la Fuente-Sandoval et al, 2013b).

Symptoms were assessed with the PANSS scale as described above, with an emphasis on probing for auditory hallucinatory experiences, during the PANSS interview. There were 26 (20 males and 6 females) healthy control subjects (Cntrl group). One of the Schz group subjects had to be removed from the analyses due to poor quality of the MRS data, thus the actual number of patients was 23 (18 males and 5 females). The mean age of the Schz group was 37.65 years, SD 8.8, and 34.0, SD 8.6 for the corresponding Cntrl group subjects. There was no statistically significant difference between the groups with regard to sex and age. Mean education was 13.3 years, SD 3.0 for the Schz group subjects and 14.9 years, SD 2.5 for the Cntrl group subjects. The difference in years of education was statistically significant with a t-test, p < .05. Thus, the two groups were reasonably, although not absolutely, matched for sex and age, but not for years of education.

The study was approved by the Regional Ethical Committee for Medical Research at the University of Bergen, Norway, and signed informed consent was obtained from all subjects after they had been informed about the aims and procedure of the study. The subjects were reimbursed for travel and other expenses for participating in the study. Exclusion criteria for the healthy control subjects were any known psychiatric or neurological disorder. Exclusion criteria for both groups were any contraindication for taking part in the MR scanning, such as in-operated pacemakers or pregnancy and any known hearing deficit. All subjects were also interviewed by the MR technician before they were allowed to enter the MR scanner room for possible MR contraindications.

2.2. MR scanning

The MR-scanning was performed at Haukeland University Hospital, Bergen, Norway on a 3 T GE Signa HDxt scanner. Anatomical images were acquired with a high-resolution T1-weighted FSPGR sequence for the acquisition of 3D volume images. The parameters were: 180 sagittal slices, 256 × 256 matrix size, TE = 3.15 ms, TR = 7.95 ms, FA = 11°. The T1 images were also used for segmentation of the images into gray (GM) and white (WM) matter and cerebrospinal fluid (CSF) in frontal and temporal areas (structural data are not reported in this study, see Nygård et al., 2013). Thereafter an axial T2-weighted sequence was applied in order to guide positioning the MRS voxels of interest.

The 1H MRS data were obtained by running a single-voxel point-resolved spectroscopy, PRESS, sequence (TR = 1500 ms, TE = 35 ms, 128 averages, nominal voxel size = 20 × 20 × 20 mm) which was repeated for each of the four MRS voxels (total time = 4 × 4 min). An iterative shimming procedure was used to ensure a linewidth of less than 10 Hz and a water suppression level of at least 90%.

For the PRESS-35 sequence with the nominated voxel size and locations, a scan of 128 averages gives quite acceptable resolution, signal-to-noise and reproducibility. Although a longer scan may allow slightly improved SNR, these gains may be offset by increased susceptibility to movement artifacts and drift, perhaps increasing the effective linewidth and giving poorer overall resolution and less accurate discrimination between certain metabolites. It could also be argued that such movement artifacts may be more frequent and severe in the patients group, thus, extending scanning time above 4 min may actually induce unwanted group differences due to movement artifacts.

The Linear Combination Modelling (LCModel) software (Version 6.2-4A) was used for quantification of a the 1H MRS spectra, with only those metabolite estimates having Cramer–Rao minimum variance bounds (CRLB) of less than 20% being accepted for the statistical analyses.

The 1H MRS data were collected from four voxels that were placed in the upper posterior part of the temporal lobe, overlapping with Heschl's gyrus and primary auditory cortex, and in the left and right inferior frontal gyrus in the frontal lobe. The MNI [x, y, z] co-ordinates for the right and left center-of-gravity were [± 38, − 22.5, + 11.5 mm], for the temporal lobe voxels, and [± 16.0, + 47.5, + 3.0] for the corresponding right and left frontal lobe voxels. See Fig. 1 for example of typical voxel localizations. Fig. 2 shows an example of the MRS spectrum obtained from a single subject. The mean proportion of gray matter in each voxel and group, with standard deviations (SD) are given in Table 1. A separate ANOVA with lobe voxel (temporal, frontal) and groups (Schz, Cntrl) as factors, showed a significant main-effect of lobes, F(1,24) = 21.79, p < .001, and with higher gray matter proportion in the temporal lobe voxels. There was no significant main-effect of group, nor of the two-way interaction of lobe × group (p-values > .05).

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Fig. 1 Approximate localization of the four voxels for the MRS measurements in the left and right temporal and frontal lobes, respectively. See text Methods section for further explanations, with x-, y-, and z-MNI-coordinates for the four placements.

gr2

Fig. 2 Example of 1H MRS spectra from the left frontal voxel for a single subject. The marked peaks represent from right to left; N-acetylaspartic acid (NAA), glutamate (Glu), glutamine (Gln), and the sum of Glu + Gln (Glx), creatine (Cre), and choline (Cho). The X-axis represents parts per million (ppm), and the Y-axis represents the relative magnitude of the spectral peaks.

Table 1 Mean proportion gray matter for each of the four voxels, with standard deviations and min and max values, split for schizophrenia and healthy control groups.

Mean Minimum Maximum Std.Dev.
Group healthy controls
Left frontal 37.39173 23.84000 51.22700 7.147128
Right frontal 37.02713 23.20900 43.97700 5.028695
Right temporal 49.48835 32.32900 57.94500 6.614675
Left temporal 48.48012 26.95900 61.74400 7.312292
 
Group schizophrenia
Left frontal 31.21271 19.78900 42.16500 6.740194
Right frontal 34.09684 23.68100 42.96700 5.642468
Right temporal 44.02074 32.18800 58.22000 7.384075
Left temporal 46.15448 23.90500 58.52000 8.374661

Metabolite estimates from the MRS data were scaled to a water reference, and adjusted for local water concentration to account for partial volume effects. Water concentration was derived from estimated GM, WM and CSF proportions measured in the respective voxel of interest, using standard values for the density in each tissue class. Tissue class segmentation for this purpose was obtained from each subject's segmented T1 structural image after co-registering to the T2 structural image upon which the voxels had been positioned. Segmentation was performed using in-house scripts, based on SPM8's combined segmentation and spatial normalization procedures. Considering the spectral overlap of glutamate and glutamine components at 3 T (and consequent difficulty in reliably discriminating between the two), a combined estimate denoted Glx was used for further analysis. Glx estimates were numerically marginally more reliable than those for Glu alone, displayed similar tendencies and appeared to be largely driven by the latter component.

To control for potential confounding from individual variability in brain anatomy, affecting the amount of GM in the MRS voxels, we calculated the ratio of Glx level and GM density in the voxels by using an aggregate measure for all four voxels. A one-way ANOVA showed that the group difference was maintained also when controlling for variability in GM density between the subjects. To control for differences between the groups in proportion of GM of the total brain volume, we conducted a t-test between the groups. This test showed no significant difference between the Schz and Cntrl groups for proportion of GM of the total brain volume, t(48) = 1.78, n.s. Means for the respective proportions were 0.579 for the Schz group and 0.598 for the Cntrl group.

The MRS data were collected as part of a larger MR study on schizophrenia, where also functional MRI (fMRI) and behavioral data were collected (reported separately in Nygård et al, 2012 and Løberg et al, 2012). For the fMRI part, the subjects were presented with a dichotic attention/executive functions task (see Westerhausen et al., 2010 for details). This paradigm is frequently used in clinical studies, also including schizophrenia patients (e.g. Green et al, 1994 and Løberg et al, 1999) to study cognitive impairment.

2.3. Data processing and analysis

MRS data were first subjected to analyses of variance (ANOVAs) based on patient versus control group comparisons, and then for comparisons between high versus low symptom load sub-groups for the patients only. The PANSS scores were used to define high and low symptom loads, with scores of < 4 defining low symptom load (LSL) and scores from 4 and above defining high symptom load (HSL). There were 16 and 7 patients in the LSL and HSL sub-groups for the temporal lobe P3 symptom data, and 18 and 5 for the corresponding N2 symptom sub-groups. Individual spectra were required to meet certain quality constraints, on signal quality (linewidth ≤ 14, SNR > 8) and CRLB of metabolite estimates (< 20%); spectra were also inspected visually to check for aberrations. Rejection of spectra not meeting our quality criteria left several gaps in the usable data — many subjects having good data for some but not all of the four voxels, making direct comparison between the voxels less meaningful. Therefore, to maintain statistical power, Glx estimates were pooled for the left and right sides.

The n for the temporal lobe data was 26 for the Cntrl group and 23 for Schz group, while the corresponding n for the frontal lobe data were 26 for the Cntrl and 18 for the Schz group, respectively. In addition, correlations between the Glu data aggregates and PANSS symptom scores were performed. The correlations involved the P3 (Hallucinatory behavior) symptom, and the negative N2 (Emotional withdrawal) symptom, in addition to correlations with the sum total of Positive and Negative symptom scores. We chose the N2 symptom to contrast with the P3 symptom because this contrast was used in two previous studies in our laboratory when studying perceptual and cognitive aspects of auditory hallucinations (Hugdahl et al., 2012). Since the PANSS scores are restricted interval scale data (range 1–7), non-parametric Spearman correlation coefficients were calculated for the correlations with single symptom scores, while parametric Pearson correlation coefficients were calculated for the sum total scores of the Positive and Negative symptom scales, which are ordinal scale data. All significance tests were two-tailed.

3. Results

3.1. Group comparisons

3.1.1. Schz vs. Cntrl groups

A first analysis was based on the average of Glx levels for the two sub-aggregates. The ANOVA was performed on a 2 (Groups) × 2 (Lobes) design. This analysis showed two effects. First, there was a significant main-effect of Groups, F(1,43) = 8.264, p = .006, second there was a significant main-effect of Lobes, F(1,43) = 13.325, p < .001. The respective means for the two main-effects are shown in Fig. 3 as scatter-grams.

gr3

Fig. 3 Scatter-grams of Glx levels for the Schz and Cntrl groups, split for the temporal and frontal lobe voxels, adjusted for local water concentration. Whiskers = mean and 95% confidence interval.

As can be seen in Fig. 3, the Schz group had lower Glx levels compared to the Cntrl group in both the temporal and frontal lobe regions. Glx levels were also overall lower in the frontal compared to the temporal lobe regions. Planned comparisons for simple-effects showed that the group difference was significant for both the temporal F(43) = 5.77, p = .020, and the frontal lobe region, F(43) = 7.64, p = .008.

3.1.2. Low vs. high symptom load in the patients

A second series of ANOVA analyses involved splitting the data for the Schz group into low (LSL) versus high (HSL) symptom load sub-groups, based on the PANSS scores, and as described in the Methods section. The first of these ANOVAs involved the P3 symptom scores, separately for the pooled temporal and frontal data aggregates. Because of the uneven number of subjects with valid Glx data for the temporal and frontal lobe voxels, we performed separate ANOVAs for the two regions. The ANOVA for the pooled temporal lobe voxels showed a significant difference for the two sub-groups, F(1,21) = 5.072, p = .022, with higher Glx levels in the HSL sub-group compared to the LSL sub-group. The ANOVA for the pooled frontal lobe voxels also showed a significant difference for the two sub-groups, F(1,17) = 9.684, p = .006 and, again, with higher Glx levels in the HSL sub-group compared to the LSL sub-group, and similar to the Cntrl group. The means for the two sub-groups and temporal vs. frontal lobe regions are shown as scatter-grams in Fig. 4.

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Fig. 4 Scatter-grams of Glx levels adjusted for local water concentration, for the Schz group, split for PANSS P3 Hallucinatory behavior scores below 4 = low symptom load (LSL) sub-group, and 4 and higher = high symptom load (HSL) sub-group. Whiskers = mean and 95% confidence interval.

We next performed planned comparisons for differences in Glx levels and symptom load for the N2 Negative symptom. The ANOVAs showed no significant differences, neither for the HSL, nor for the LSL sub-group, see means in Fig. 5 as scatter-grams.

gr5

Fig. 5 Scatter-grams of Glx levels adjusted for local water concentration, for the Schz group, split for PANSS N2 Emotional withdrawal scores below 4 = low symptom load (LSL) sub-group, and 4 and higher = high symptom load (HSL) sub-group. Whiskers = mean and 95% confidence interval.

3.2. Correlations between symptom scores and Glx levels

In order to further elucidate the relationship between symptom loadings for the P3, N2, sum total of Positive and Negative symptoms and Glx levels in the temporal and frontal lobe, we performed a series of parametric and non-parametric correlations, as described in the Methods section.

We first calculated the correlations for the P3 Hallucinatory behavior symptom score and Glx level. The correlation coefficient for the temporal regions was r = .490, p < .05. The corresponding correlation coefficient for the frontal regions was r = .551, p < .05. Thus, there were significant positive correlations between scores on the P3 hallucination item and Glx levels in both temporal and frontal regions. The next series of correlations were for the N2, Emotional withdrawal symptom and Glx levels. The correlations for the N2 symptom scores and Glx levels were non-significant for both the temporal and frontal lobe regions, r = .027, n.s. for the temporal lobe region, and r = .141, n.s. for the frontal lobe region. Thus, there were no significant relationship between Glx levels and the N2 symptom scores. The correlations for the P3 and N2 symptoms and Glx levels, for both temporal and frontal lobe areas, are shown as scatter-plots in Fig. 6.

gr6

Fig. 6 Scatter-plots of the correlation between PANSS P3 hallucinatory behavior scores and Glx levels. Striped bands around the regression line = 95% confidence intervals. See text in the Results section for further explanations of the correlations.

A second series of correlations were performed for the sum total of Positive and Negative PANSS scores and Glx levels in the temporal frontal regions, respectively. The correlations for the sum total of Positive symptoms and temporal lobe Glx level was, r = .535, p < .05, and r = .634, p < .05 for the frontal lobe. The corresponding scatter-plots are seen in Fig. 7 upper two panels.

gr7

Fig. 7 Scatter-plots of the correlation between PANSS N2 emotional withdrawal scores and Glx levels. Striped bands around the regression line = 95% confidence intervals. See text in the Results section for further explanations of the correlations.

Bonferroni corrections (α / 2, p = 0.025, because of separate tests for temporal and frontal lobe data) for multiple correlations retained all the significant correlations.

The correlations for the sum total of Negative symptom scores and Glu levels were negative and non-significant, r = − .031, n.s. for the temporal lobe, and r = − .079, n.s. for the frontal lobe. Thus, there were no significant associations for the sum total of Negative symptoms. The corresponding scatter plots are seen in Fig. 7 lower two panels.

4. Discussion

The main findings of the present study was that the Schz group as a whole had significantly reduced Glu(Glx) levels compared to the Cntrl group in both temporal and frontal lobe areas. This is in line with previous research that has found abnormal glutamate regulation in schizophrenia patients (e.g. Coyle et al., 2012 for review). Marsman et al. (2013) also reported reduced Glu levels in the frontal lobe in schizophrenia patients compared to healthy controls in a meta-analysis of 28 studies and 647 patients, based on 1H MRS measurements, although it is unclear how much of this was due to medication effects (cf. Kegeles et al., 2012). A second main finding was that Glu(Glx) levels were increased in patients in the HSL sub-group, which means that higher scores on the PANSS hallucination item were associated with significantly increased Glu(Glx) levels, which is a novel finding. Since the data were pooled in the main analysis for the left and right hemisphere voxels, due to loss of data, we performed a separate ANOVA with hemisphere sides as a factor in the analysis. This ANOVA showed no significant effects of hemisphere side, only a significant main-effect of lobes, which was also found in the main analysis.

Although not a focus in the present study, the Schz group performed significantly worse than the Cntrl group on the attention/executive dichotic listening task, which also could be related to altered Glu(Glx) function, since previous studies have shown that Glu is involved in the regulation of brain activation in this task (Wageningen et al, 2010, Wageningen et al, 2009, and Falkenberg et al, 2012). The relationship between Glu and schizophrenia may however be confounded by antipsychotic medication. For example, Kegeles et al. (2012) found that Glx levels were significantly elevated in unmedicated patients compared to healthy controls, which could indicate a kind of normalization of Glx levels by the medication. Similarly (de la Fuente-Sandoval et al, 2011), (de la Fuente-Sandoval et al, 2013a), and (de la Fuente-Sandoval et al, 2013b) found Glu elevation in first-episode and prodromal patients. The results in the Kegeles et al. (2012) study did however not show a significant difference for Glx between the medicated and unmedicated patients, thus, it is unclear how antipsychotic medication may affect Glu and Glx levels in the brain. It should further be pointed out that none of these studies looked specifically for associations between AVH and Glu(Glx) levels, and while Kegeles et al. (2012) had frontal lobe voxel placements, de la Fuente-Sandoval had striatal and cerebellar placements.

The present results may indicate that abnormality of the classic dopamine (DA)–Glu hypothesis in schizophrenia may be dependent on the symptom loading profile of the patient. This has not previously been reported. The fact that Glu(Glx) levels were significantly increased in the P3 HSL sub-group compared to the P3 LSL sub-group may thus point towards an alternative hypothesis; that glutamatergic hyper-activity is not kept in balance by corresponding increased GABA release to inhibit excessive Glu release in frontal and temporal areas, because of a specific glutamate-GABA deficit that is underlying auditory hallucinations (cf. Carlsson et al, 2001, Lewis and Sweet, 2009, and Lewis, 2011). Thus, auditory hallucinations may be the result not only of striatal dopamine excess at D2-receptors, as the classic model predicts, but also of glutamate over-activation in cortical regions. Such a hypothesis has the advantage of being parsimonious and closer to the neuroanatomical substrates of auditory hallucinations (Gaser et al, 2004, Neckelmann et al, 2006, Modinos et al, 2013, and van Tol et al, 2013). It should be noted that one of the few MRS studies where low and high symptom loads has been compared (Homan et al., 2014) in fact found increased NAA metabolite levels in the superior temporal gyrus area in hallucinating compared to non-hallucinating patients.

An interesting implication of the present findings is that the patients in the P3 HSL group by definition also would be classified as treatment resistant, such that increased Glu levels would go together with resistance to treatment and to reduction of Positive symptoms. This is in line with a recent report by Demjaha et al. (2014) who found an inverse relationship between Glu and DA in treatment resistant schizophrenia patients, with elevated Glu levels in the prefrontal cortex but normal striatal DA function. We now suggest that this relationship may be especially salient for hallucinations.

A prediction that would follow from an alternative hypothesis is that increased Glu levels should be specific for Hallucinations and Positive symptoms, which was confirmed by the results, since there were no significant correlations with Negative symptoms, neither for a single Negative symptom, nor for the total sum of Negative symptoms. The specific nature of Glu(Glx) level increase in the P3 HSL sub-group was additionally tested by performing a corresponding analysis for the P1 Delusions symptom, which also showed a positive correlation with Glu(Glx) levels, with higher levels in the P1 HSL sub-group compared to the LSL sub-group. We also did a corresponding additional analysis for the N1 Affective blunting symptom, comparing the N1 HSL and LSL sub-groups. This analysis showed no significant differences, the results were in fact more or less the same as for the N2 Negative symptom. That hallucinations and delusions share associations with Glu levels would be expected since these symptoms are clinically closely related, where patients with frequent auditory hallucination often also have delusionary thoughts and behaviors. Thus, the fact that delusions share the findings seen for hallucinations in the present study does support the conclusions.

A limitation with the present study is the relatively small number of patients, and particularly when splitting for the HSL and LSL sub-groups. This is a true weakness, although the scatter-plots in Fig 6 and Fig 7, and the significant correlations for the P3 (and P1) scores, and the absence of significant associations for the N2 (and N1) scores speaks to the reliability of the findings. Further, as described above, Homan et al. (2014) reported a similar positive relation for temporal lobe NAA levels and hallucinating versus non-hallucinating patients, compared with a healthy control group, where the numbers of subjects were 12, 8, and 11, respectively. Thus, significant differences in levels of brain metabolites between sub-groups of patients with frequent and infrequent hallucinations can be obtained despite small group sizes. Another limitation is the absence of an unmedicated group to compare effects of medication on Glu(Glx) levels, since previous studies have shown increased levels in first-episode and prodromal patients (de la Fuente-Sandoval et al, 2013a and de la Fuente-Sandoval et al, 2013b), that should be addressed in future research on the neurochemistry of auditory hallucinations. To conclude, the present findings of reduced Glu(Glx) levels in schizophrenia compared to controls in both temporal and frontal lobe areas, and an association to verbal hallucinations support the hypothesis that altered glutamate activity in temporal and frontal lobe is related to pathophysiological mechanisms of psychosis.

Role of the funding source

The funding sources are Research Council of Norway (RCN) and the European Research Council (ERC). The role of the funding sources has been to fund the study from its inception to the writing of the manuscript, after applications for funding having been submitted to the respective funding agents.

Contributors

Kenneth Hugdahl analyzed the data and wrote the manuscript (ms).

Alexander Craven assisted in analyzing the data and wrote parts of the methods section.

Merethe Nygård collected the data and commented on the ms.

Else-Marie Løberg recruited the patients and commented on the ms.

Jan Øystein Berle recruited the patients and commented on the ms.

Erik Johnsen recruited the patients, wrote parts of the methods section, and commented on the ms.

Rune Kroken recruited the patients and commented on the ms.

Karsten Specht assisted in analyzing the data and commented on the ms.

Lars Ersland assisted in analyzing the data and commented on the ms.

Ole A. Andreassen assisted in interpreting the results and commented on the ms.

Conflict of interest

Dr. Hugdahl is a co-founder and share-holder in NordicNeuroLab Inc. which manufactured the video-goggles and headphones used in the MR investigation. He is the PI recipient of the EU-ERC Advanced Grant #249516, Research Council of Norway (RCN) grant #807695, and Helse Vest grant #911793. Dr. Hugdahl declares no conflicts of interest.

Dr. Andreassen has received speaker's honorarium from GSK, Eli Lilly, Lundbeck, and Otsuka. The research was supported by the Research Council of Norway (RCN) grants #213837 and #223273, South-East Norway Health Authority #2013-123 and K.G. Jebsen Foundation. He declares no conflicts of interest.

Dr. Johnsen has no financial disclosures, nor any competing interests. He is the PI recipient of the Research Council of Norway grant #213727 and Helse Vest grants #911679, #911820, and #911876.

Dr. Kroken owns no shares and declares no conflicts of interest.

Dr. Berle is participating in an Advisory Board of Eli Lilly & Co., Norway. He has within the last three years received speaker's honoraria from BioPhausia AB/Medivir AB, Eli Lilly & Co., H. Lundbeck A/S and Otsuka Pharmaceutical Europe Ltd. He declares no conflicts of interest.

Dr. Nygård reports no financial relationships with commercial interest. He reports no competing interests.

Dr. Løberg reports no financial relationships with commercial interests. She is the recipient of Research Council of Norway (RCN) grant #134088/320. She declares no conflicts of interest.

Dr. Ersland is a cofounder, board member and shareholder in NordicNeuroLab Inc. NordicNeuroLab manufactured the video-goggles and headphones that were used in the MR investigation. He declares no conflicts of interest.

Mr. Craven declares no financial, nor any interest conflicts.

Dr. Specht owns shares in the NordicNeuroLab Inc., which manufactured the video-goggles and headphones used in the MR investigation. Dr. Specht declares no conflicts of interest.

Acknowledgment

The present study was funded by ERC Advanced Grant #249516 “VOICE” and RCN FRIBIOMED #807698 grant to Kenneth Hugdahl, and RCN grant #134088/320 to Else-Marie Løberg, and a RCN Center of Excellence grant #222373 to Ole Andreassen. The authors want to express their thanks to Roger Barndon, Eva Øksnes and Turid Randa for running the MR scanner, and to professor emeritus Hugo Jørgensen for PANSS interviewing some of the patients.

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Footnotes

a Department of Biological and Medical Psychology, University of Bergen, Norway

b Division of Psychiatry, Haukeland University Hospital, Bergen, Norway

c Department of Clinical Psychology, University of Bergen, Norway

d Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway

e Department of Radiology, Haukeland University Hospital, Bergen, Norway

f Section of Psychiatry, Department of Clinical Medicine, University of Bergen, Bergen, Norway

g Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway

h NORMENT Center of Excellence, University of Oslo and Oslo University Hospital, Norway

Corresponding author at: Department of Biological and Medical Psychology, University of Bergen, Jonas Lies vei 91, N-5009 Bergen, Norway.

1 Because the Glu peak is sometimes difficult to identify in individual spectra, due to overlap with glutamine (Gln), see Methods section for details, it is common to measure the combined glutamate and glutamine levels, called Glx. To indicate that we have measured Glx when discussing glutamate levels we use the label "Glx" or "Glu(Glx)" when specifically referring to the results in the present study.