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Resting-state gamma-band power alterations in schizophrenia reveal E/I-balance abnormalities across illness-stages

  1. Tineke Grent-'t-Jong
  2. Joachim Gross
  3. Jozien Goense
  4. Michael Wibral
  5. Ruchika Gajwani
  6. Andrew I Gumley
  7. Stephen M Lawrie
  8. Matthias Schwannauer
  9. Frauke Schultze-Lutter
  10. Tobias Navarro Schröder
  11. Dagmar Koethe
  12. F Markus Leweke
  13. Wolf Singer
  14. Peter J Uhlhaas  Is a corresponding author
  1. University of Glasgow, United Kingdom
  2. Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Germany
  3. Goethe University, Germany
  4. Institute of Health and Wellbeing, University of Glasgow, United Kingdom
  5. University of Edinburgh, United Kingdom
  6. University Edinburgh, United Kingdom
  7. University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
  8. Medical Faculty, Heinrich-Heine-University, Germany
  9. Norwegian University of Science and Technology, Norway
  10. Central Institute of Mental health, Medical Faculty Mannheim, Heidelberg University, Germany
  11. University of Sydney, Australia
  12. Medical Faculty Mannheim, Heidelberg University, Germany
  13. Max Planck Institute for Brain Research, Germany
  14. Ernst Strüngmann Institute for Neuroscience and the Max Planck Society, Germany
  15. Frankfurt Institute for Advanced Studies, Germany
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Cite this article as: eLife 2018;7:e37799 doi: 10.7554/eLife.37799

Abstract

We examined alterations in E/I-balance in schizophrenia (ScZ) through measurements of resting-state gamma-band activity in participants meeting clinical high-risk (CHR) criteria (n = 88), 21 first episode (FEP) patients and 34 chronic ScZ-patients. Furthermore, MRS-data were obtained in CHR-participants and matched controls. Magnetoencephalographic (MEG) resting-state activity was examined at source level and MEG-data were correlated with neuropsychological scores and clinical symptoms. CHR-participants were characterized by increased 64–90 Hz power. In contrast, FEP- and ScZ-patients showed aberrant spectral power at both low- and high gamma-band frequencies. MRS-data showed a shift in E/I-balance toward increased excitation in CHR-participants, which correlated with increased occipital gamma-band power. Finally, neuropsychological deficits and clinical symptoms in FEP and ScZ-patients were correlated with reduced gamma band-activity, while elevated psychotic symptoms in the CHR group showed the opposite relationship. The current study suggests that resting-state gamma-band power and altered Glx/GABA ratio indicate changes in E/I-balance parameters across illness stages in ScZ.

https://doi.org/10.7554/eLife.37799.001

Introduction

Emerging evidence suggests that efficient information transfer in neural networks depends crucially upon the balance between excitation and inhibition (E/I-Balance) (Shu et al., 2003; Haider et al., 2006). A shift in E/I-balance towards elevated excitability has been recently implicated in the pathophysiology of schizophrenia (ScZ) (Driesen et al., 2013; Lisman, 2012; Murray et al., 2014; Uhlhaas and Singer, 2012) and could provide a crucial intermediate phenotype that links basic circuit abnormalities with observations from non-invasive neuroimaging. However, it is currently unclear when such abnormalities arise in the course of ScZ and their relationship to clinical and behavioural features associated with the syndrome.

Among the circuit mechanisms that are involved in the maintenance of E/I-balance, parvalbumin-expressing (PV+) γ-Aminobutyric acid (GABA)ergic interneurons are of particular interest (Xue et al., 2014) as inhibition of pyramidal cell activity regulates the output of cell-assemblies and leads to rhythmic fluctuations in excitability or neural oscillations (Sohal et al., 2009; Kopell and LeMasson, 1994). Moreover, there is consistent evidence that α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)- and N-methyl-D-aspartate Receptor (NMDA-R)-mediated activation of PV+ interneurons is essential for the generation of oscillatory activity (Carlén et al., 2012; Fuchs et al., 2007), especially at gamma-band (30–90 Hz) frequencies. In ScZ, converging evidence from genetics (Pocklington et al., 2015), post-mortem data (Lewis et al., 2012) and brain imaging (Kegeles et al., 2012) have supported the possibility that E/I-balance is disrupted which is consistent with observations from electro/magnetoencephalographical (EEG/MEG)-data that task-related, gamma-band oscillations are reduced (Uhlhaas and Singer, 2010).

One central prediction for a shift in E/I-balance in ScZ towards increased excitability-levels is an increase in spontaneous gamma-band activity and a wealth of evidence from pre-clinical (Yizhar et al., 2011; Kocsis, 2012; Pinault, 2008) as well as data in healthy controls following NMDA-R hypofunctioning (Rivolta et al., 2015; Shaw et al., 2015) highlight that transient increases in excitability are associated with enhanced occurrence of gamma-band power. For example, NMDA-R antagonists have been show to increase spontaneous gamma-band activity in both human (Rivolta et al., 2015) and pre-clinical research (Saunders et al., 2012).

Further support for the E/I-balance hypothesis comes from Magnetic Resonance Spectroscopy (MRS) studies that have investigated alterations in Glutamate and GABA-concentrations across cortical and subcortical areas. A consistent finding is an elevation of Glutamate-levels across illness-stages in ScZ (Merritt et al., 2016) while the evidence for changes in GABA-levels is less consistent (Egerton et al., 2017), supporting the view for a shift towards increased excitability of neural circuits.

To provide further critical support for the E/I-balance hypothesis, we applied an advanced MEG approach to examine resting-state MEG-recordings in participants meeting clinical high-risk criteria (CHR), first-episode (FEP) and chronic ScZ-patients. Currently, there is only limited evidence available from EEG/MEG-recordings in ScZ-patients (Rutter et al., 2009; Andreou et al., 2015; Ramyead et al., 2015), which has tested comprehensively the pattern of spontaneous gamma-band activity across illness-stages. MEG is characterized by an improved signal-to-noise ratio for measurements of high-frequency oscillations compared to EEG (Muthukumaraswamy and Singh, 2013). MEG is also ideally suited for source-reconstruction that allows the identification of the anatomical lay-out of resting-state networks with high spatial resolution.

Accordingly, we focused on the following questions: (1) Are there differences in resting-state gamma-band networks in ScZ and what is the direction of effects across different illness-stages? Previous data highlighted that the signatures of fMRI resting-state networks during early stage psychosis, but not in chronic ScZ, resemble the acute effects of NMDA-R hypofunctioning (Anticevic et al., 2015). In addition, there is evidence that glutamatergic neurotransmission is increased in younger ScZ-patients (Marsman et al., 2013). Accordingly, we predicted that gamma-band activity in CHR and possibly FEP-patients would be upregulated, while in chronic ScZ-patients the opposite pattern would occur. (2) Are gamma-band fluctuations related to clinical symptoms and cognitive deficits in ScZ? Because of the role E/I-balance in shaping information transfer across large-scale networks (Shu et al., 2003; Yizhar et al., 2011), we expected that alterations in gamma-band power would closely correlate with neurocognitive deficits and clinical symptoms across clinical groups. (3) What is the nature of alterations in resting-state gamma-band activity in ScZ? Changes could involve band-limited as opposed to alterations across the entire gamma-band frequency with important implications for the interpretation of these phenomena. And (4) Are alterations in gamma-band power in CHR-participants related to changes in GABA and Glutamate/Glutamine (Glx) concentrations? Based on the relationship between E/I-balance and gamma-band power (Yizhar et al., 2011), we predicted that CHR-participants would be characterized by an altered Glx/GABA-ratio that correlates with increased high-frequency activity.

Results

Demographic and Clinical Characteristics

Table 1 summarizes demographic and clinical characteristics of participant groups. PANSS and neurocognition data were available only for a subset of chronic ScZ and FEP-patients. The chronic ScZ-group was significantly older than the control participants. There were also significantly more females in the CHR than in the FEP and chronic ScZ-groups. FEP-patients were characterized by higher ratings on the Excitation, Cognitive, Positive and Depression PANSS subscales and total PANSS-scores than the chronic ScZ group. Neurocognitive data showed an overall increase in severity and range of cognitive deficits across the course of illness.

Table 1
Demographical and clinical data.
https://doi.org/10.7554/eLife.37799.002
CHR
(n = 88)
CON1
(n = 48)
FEP
(n = 21)
SCZ
(n = 34)
CON2
(n = 37)
GROUP effect*Pairwise comparisons*H/p -values
Age (mean/SEM)
22.0/0.522.7/0.527.0/1.537.1/2.028.6/1.2H(4)=80.8
p<0.0001
CHR vs. FEP
CHR vs. SCZ
−54.6/0.006
−104.5/0.000
Sex (mean/SEM)
female/male67/2133/155/1612/2213/24H(4)=38.9
p<0.0001
FEP vs. CHR
CON2 vs.CON1
59.6/0.000
38.2/0.020
Education (mean/SEM)
Years15.5/0.516.6/0.414.1/0.714.2/0.616.6/0.6H(4)=16.7
p=0.002
CON1 vs. SCZ41.8/0.027
BACS(mean/SEM)CHR
(n = 88)
CON1
(n = 48)
FEP
(n = 18)
SCZ
(n = 28)
CON2
(n = 37)
GROUP effectPairwise comparisonsH/p -values
Verbal Memory−0.36/0.170.23/0.17−0.41/0.38−0.93/0.240.79/0.14H(4)=26.5
p<0.0001
SCZ vs. CON2−76.1/0.000
Digit Sequencing−0.39/0.12−0.07/0.110.26/0.36−1.07/0.200.62/0.17H(4)=35.5
p<0.0001
SCZ vs. FEP
SCZ vs. CHR
SCZ vs. CON2
66.9/0.003
38.6/0.036
−90.1/0.000
Token Motor Task−0.64/0.150.28/0.160.60/0.270.47/0.211.39/0.15H(4)=56.9
p<0.0001
SCZ vs. CHR
CHR vs. CON1
CHR vs. FEP
SCZ vs. CON2
46.9/0.004
−37.8/0.005
−54.5/0.006
−45.3/0.050
Verbal Fluency0.15/0.120.38/0.19−0.85/0.49−0.90/0.200.64/0.21H(4)=27.1
p<0.0001
SCZ vs. CHR
FEP vs. CON2
SCZ vs. CON2
52.0/0.001
−51.7/0.000
−73.3/0.000
Symbol Coding−0.04/0.140.62/0.16−0.96/0.27−1.19/0.23−0.26/0.15H(4)=46.6
p<0.0001
SCZ vs. CHR
FEP vs. CHR
SCZ vs. CON2
CHR vs. CON1
57.0/0.000
44.5/0.049
−48.0/0.030
−32.4/0.031
Tower of London0.18/0.120.28/0.100.51/0.24−0.19/0.210.85/0.13H(4)=15.0
p<0.0001
SCZ vs. CON2−76.1/0.000
COMPOSITE score−0.31/0.140.46/0.10−0.22/0.35−1.03/0.211.11/0.11H(4)=61.0
p<0.0001
SCZ vs. CON2
FEP vs. CON2
CHR vs. CON1
−111.3/0.000
−72.1/0.001
−38.5/0.004
PANSS (mean/SEM)FEP
(n = 16)
SCZ
(n = 30)
GROUP effect
Negative18.0/1.316.6/1.1not sign diff
Excitation9.4/0.87.2/0.7H(1)=6.1, p=0.013
Cognitive12.3/1.110.5/0.7not sign diff
Positive12.5/0.79.8/0.7H(1)=5.1, p=0.024
Depression14.8/1.112.2/0.6H(1)=3.9, p=0.047
TOTAL66.9/3.256.3/3.0H(1)=5.4, p=0.020
CAARMS (mean/SEM) *frequencyCHR (n = 88)SPI-A (n = 25)CAARMS (n = 29)BOTH(n = 34)GROUP effectPairwise comparisonsH/p -values
Unusual Thought Content5.2/0.83.6/1.43.9/1.17.6/1.3H(2)=6.8
p=0.033
not sign diff
Non-Bizarre Ideas9.9/0.85.6/1.19.7/1.413.3/1.3H(2)=14.3
p=0.001
SPI-A vs. SPI-A+CAARMS−25.2/0.000
Perceptual Abnormalities8.1/0.73.9/0.79.4/1.310.2/1.1H(2)=15.7
p<0.0001
SPI-A vs. SPI-A+CAARMS
SPI-A vs. SPI-A+CAARMS
−21.5/0.006
−25.2/0.000
Disorganized Speech4.3/0.63.8/0.92.1/0.86.5/0.9H(2)=11.9
p=0.003
CAARMS vs. SPI-A+CAARMS−20.8/0.002
TOTAL27.6/1.816.8/2.925.0/2.437.6/2.8H(2)=22.2
p<0.0001
SPI-A vs. SPI-A+CAARMS
CAARMS vs. SPI-A+CAARMS
−31.4/0.000
−17.4/0.021
Global Functioning (GAF: mean/SEM)CHR
(n = 88)
CON1
(n = 48)
GROUP effect
59.8/1.287.4/1.0H(1)=81.0, p<0.0001
MEDICATIONCHR
(n = 88)
CON1
(n = 48)
None3946
Anti-psychotic10
Mood-stabilizer10
Anti-depressant200
Anti-convulsant00
Other110
Multiple162
  1. *Kruskal-Wallis independent-sample test. Alpha-level 0.05, two-sided with p-values adjusted for ties.

    †Kruskal-Wallis independent-sample test performed on z-standardized data (Keefe et al., 2008). Alpha-level 0.05, two-sided, p-values adjusted for ties.

Resting-State Gamma-Band Power Across Illness-Stages in ScZ

Gamma-band resting-state power, separated in both low (30 – 46 Hz) and high (64 – 90 Hz) gamma-band ranges, was estimated using the Dynamic Imaging of Coherence Sources (DICS) beamforming approach (Gross et al., 2001). Main contrasts included (1) 88 CHR-participants against 48 controls (CON1), (2) 21 FEP-patients, and (3) 34 chronic SCZ-patients, both against a second set of 37 controls (CON2).

Low Gamma-Band (30–46 Hz) power

Significant differences from control data were observed for FEP and chronic ScZ groups, but not for CHR-participants (Figure 1). FEP-patients showed significantly decreased prefrontal cortex low gamma-band activity (−2.15 < t(56)<−3.79, 0.002 < p < 0.006; see Table 2 for specific locations), while occipital cortex activity was increased (2.82 < t(56)<3.80, 0.002 < p < 0.006). In contrast, chronic ScZ patients showed widespread decreased low gamma-band activity in frontal, temporal and sensorimotor areas (−2.35 < t(69)<−4.24, 0.002 < p < 0.006).

Whole-Brain Gamma-Band Power Group Differences Across Illness-Stages.

(A) Low gamma (30–46 Hz) source-power differences for the three main group contrasts: CHR vs.CON1 (left panel), FEP vs.CON2 (middle panel), ScZ vs.CON2 (right panel). Sources were estimated using a DICS beamformer method. Slice- and surface plot representations are shown with t-values corresponding to significant voxels (non-parametric, Monte-Carlo permutation based independent t-tests, FDR corrected at p<0.05, two-sided). Red colors (positive t-values) indicate an increase in gamma-band power compared to controls, whereas blue colors (negative t-values) reflect decreased gamma-band power in the clinical groups. (B) As panel A, but for high gamma (64 – 90 Hz) band activity.

https://doi.org/10.7554/eLife.37799.003
Table 2
Overview of AAL regions of significantly modulated resting-state low and high gamma-band power.
https://doi.org/10.7554/eLife.37799.004
Group contrastLabels of significant AAL regions*t-values (range)p-values (range)
Low GAMMA (30–46 Hz)
FEP vs CON2left Calcarine Fissure,
left Inferior Occipital Gyrus
2.82 to 3.800.002–0.006
right and left Superior Medial Frontal Gyrus,
right Middle Frontal Gyrus
−2.15 to −3.790.002–0.006
SCZ vs CON2right and left Superior Medial Frontal Gyrus,
right Middle Frontal Gyrus,
left Inferior Parietal Lobule,
left Superior Orbital Frontal Gyrus,
left Superior Temporal Gyrus,
left PostCentral Gyrus,
right PreCentral Gyrus
−2.35 to −4.240.002–0.006
High GAMMA (64–90 Hz)
CHR vs CON1left Middle Occipital Gyrus,
right and left Middle Frontal Gyrus,
left Angular Gyrus,
left Inferior Parietal Lobule
2.40 to 2.740.002–0.006
SPI-A only vs CON1No significant voxels
CAARMS only vs CON1left Middle Frontal Gyrus,
left Middle Occipital Gyrus
2.670.006
CAARMS + SPI A vs CON1right and left Middle Occipital Gyrus,
right and left Middle Frontal Gyrus.
left Angular Gyrus,
right Inferior Parietal Lobule, left Superior Medial Frontal Gyrus
2.16 to 3.430.002–0.006
FEP vs CON2right and left Calcarine Fissure, right and left Inferior Occipital Gyrus,
right and left Middle Occipital Gyrus,right and left PreCuneus,
left Inferior Frontal Gyrus,left Angular Gyrus
2.48 to 4.080.002–0.006
right Middle Frontal Gyrus−2.42 to −3.260.002–0.006
SCZ vs CON2right and left Superior Medial Frontal Gyrus,
left Superior Orbital Frontal Gyrus, left Middle Orbital Frontal Gyrus, left PostCentral Gyrus
−2.40 to −3.560.002–0.006
  1. *Non-parametric Monte-Carlo permutation based independent-sample tests, alpha-level 0.05, two-sided, FDR corrected voxels.

High Gamma-Band (64–90 Hz) power 

Significant differences were found for all clinical groups in the 64–90 Hz range (Figure 1). A significant increase in high gamma-band power was found in both midfrontal and posterior-occipital and angular gyrus in CHR-participants (2.40 < t(134)<2.74, 0.002 < p < 0.006). In FEP and ScZ-patients, changes in high gamma-band power were comparable to those seen at lower gamma-band frequencies, with strong increases in posterior regions for the FEP-group (2.48 < t(56)<4.08, 0.002 < p < 0.006) and moderate decreases in frontal high gamma-band power in both FEP (−2.42 < t(56)<−3.26, 0.002 < p < 0.006) and chronic SCZ-patients (−2.40 < t(69)<−3.56, 0.002 < p < 0.006).

Resting-State Gamma-Band Power in CHR-Subgroups

We also assessed changes in gamma-band power in CHR-subgroups based on whether they met CHR-criteria for Basic Symptoms as assessed by the Schizophrenia Proneness Instrument, Adult version (SPI-A) (Schultze-Lutter et al., 2007), attenuated psychotic symptoms defined by the Comprehensive Assessment of At Risk Mental States (CAARMS) interview (Yung et al., 2005) or on both measures. Previous data (Schultze-Lutter et al., 2014) indicated that different CHR-groups are associated with differential risks for psychosis, with CHR-participants meeting both CAARMS/SPI-A criteria having the highest risk for the development of psychosis followed by CAARMs and SPI-A only groups.

The combined SPI-A/CAARMS group was characterized by increased frontal and posterior cortex 64–90 Hz power (Figure 2: 2.16 < t(80)<3.43, 0.002 < p < 0.006) which was not present in the SPI-A only group. CHR-participants who only met CAARMS criteria showed moderately increased middle frontal and occipital cortex high gamma-band power (Figure 2: t(75) = 2.67, p=0.006).

Figure 2 with 1 supplement see all
Whole-Brain Gamma-Band Power for CHR-Groups.

(A) Low gamma-band (30–46 Hz) source-power differences for the three CHR-group contrasts: SPI-A vs.CON1 (left panel), CAARMS vs.CON1 (middle panel), CAARMS + SPI-A vs.CON1 (right panel). Sources were estimated using a DICS beamformer method. Slice- and surface plot representations are shown with t-values corresponding to significant voxels (non-parametric, Monte-Carlo permutation based independent t-tests, FDR corrected at p<0.05, two-sided). Red colors (positive t-values) indicate an increase in gamma-band power compared to controls, whereas blue colors (negative t-values) reflect decreased gamma-band power in the clinical groups. (B) As panel A, but for high gamma (64 – 90 Hz) band activity.

https://doi.org/10.7554/eLife.37799.005

Interestingly, the increase in upregulated occipital cortex high gamma-band activity in the combined SPI-A/CAARMS groups showed an overlap with the pattern observed in the FEP-group (Figure 3), but was not present in chronic ScZ-patients, whereas the down-regulated gamma-band power in frontal, temporal and sensorimotor regions was only seen in patients with ScZ but not in CHR-participants.

Illness Severity and Aberrant Gamma Activity.

Surface-projected statistical group differences in low gamma (30–46 Hz; left column) and high gamma-band (64–90 Hz; right column) for all main and the three CHR-subgroups contrasts. Values represent t-values corresponding to significant voxels (p<0.05; uncorrected, masked at critical t-values of non-parametric, Monte-Carlo permutation independent t-tests).

https://doi.org/10.7554/eLife.37799.007

Broadband vs. Band-Limited Gamma-Band Power Group Differences

We examined further the alterations in gamma-band activity to determine whether these changes encompassed specific frequency bins vs. a broad-band change across the entire gamma (30 – 90 Hz) frequency range. To this end, we examined AAL-atlas data in the gamma-band range extracted from central nodes within each significant AAL region of reported group differences (Table 2), separately for each 5 Hz bin. Statistical analyses of these data confirmed that all reported group-specific gamma-band power effects were broadband in nature (see Figure 2—figure supplement 1).

Correlations with Clinical Symptoms and Demographic Data

We also systematically explored relationships between gamma-band power and demographic data (age, sex), psychopathology (total CAARMS, total PANNS scores) and neurocognitive (composite BACS scores) variables, given recently reported strong covariation of both symptoms, age and sex on neuroimaging phenotypes and thus the need to incorporate them in evaluating patient data (Moser et al., 2018). Our goal was to determine how each factor influenced findings across the regions of significant gamma-band changes between CHR-, FEP and chronic ScZ-patients vs. controls. This approach was expected to most optimally highlight regional differences in sensitivity to each individual covariate, as the data was permuted across the covariate data rather than across gamma-band power data from all participants.

The results for low- and high gamma-band activity are summarized in Figure 4. Both total CAARMS and composite BACS scores correlated with gamma-band power, especially in the 64 – 90 Hz frequency range, in the CHR-group, suggesting that increases in gamma-band activity were related to neurocognitive deficits and elevated psychotic symptoms. Similar relationships were observed for the FEP-group for posterior areas, while frontal and central regions showed an opposite relationship. In the chronic ScZ-group, BACS and PANSS-scores were mostly correlated with a reduction of gamma-band power, especially in the lower gamma-band range.

Figure 4 with 1 supplement see all
Clinical and Demographical Variables and Gamma-band Effects.

Overview of the influence of AGE, SEX, total CAARMS, total PANSS, and composite BACS scores on low g and high gamma-band power GROUP differences. As with the main effects of GROUP, non-parametric, Monte-Carlo permutation-based independent t-test were used to test for GROUP differences, but data was permutated over the control variable data rather than the actual gamma-band source power data. The resulting remaining significant activity then represents the interaction between the main group effect and the variation in the control variable. Surface-projected interaction-effects are shown between control groups and CHR group: (top panel), FEP group (mid panel) and chronic SciZ group (lower panel).

https://doi.org/10.7554/eLife.37799.008

Across groups, modest correlations were observed between age and sex. In the chronic ScZ-group, widespread correlations at both low and high gamma-band ranges were observed with age.

The contribution of age to the main effects found in the chronic ScZ group was further investigated by repeating the main analyses on a sub-sample of age-matched ScZ (n = 25; mean age 32.2) and control participants (n = 25; mean age 31.6). The results revealed a similar pattern to those reported above (see Figure 4—figure supplement 1).

MRS-Data

MEGAPRESS MR-Spectroscopy was used to measure GABA and Glutamate/Glutamine (Glx) concentrations in the CHR and CON1 participants, focused on a 2 × 2 × 2 cm voxel covering the right middle occipital gyrus (Figure 5). Data from 69 CHR participants and 35 controls were of sufficiently high quality to use for further analyses. Results from one-way repeated-measures ANOVAs showed that, compared to controls, the CHR-group showed significantly higher excitatory Glx concentrations in right middle occipital gyrus (F(1,102) = 4.3, p=0.041, Welch-t = 5.9, p=0.017, LSD corrected), in the absence of changes in GABA concentrations (Figure 5). The imbalance in concentrations between excitatory Glx and inhibitory GABA concentrations in CHR-participants was evident also in a significantly increased Glx/GABA ratio (F(1,102) = 4.5, p=0.037, Welch-t(102)=5.8, p=0.018, LSD corrected).

Aberrant Gamma band activity is linked to changes in E/I balance.

Upper Left Panel: Data from a 2 × 2 × 2 cm voxel placed in the right Middle Occiptal Gyrus (RMOG) during 1H-MRS of GABA and Glx (Glutamate/Glutamine) concentrations (MEGAPRESS GABA editing sequence). Right Column: dot-violin distribution plots showing concentration of each metabolite (or ratio between them) for each individual participant (black dots), separately forControls (n = 35) and CHR (n = 69) participants. Red lines indicate median concentration (middle line) and 1st and 3rd quartiles of the distribution. Data was tested for statistical group differences, using one-way repeated-measures ANOVAs, followed up by post-hoc Welch t-tests (bootstrapping: n = 1000, LSD corrected for multiple comparisons). Significant increases were found for CHRs, compared to CONs, in both Glx concentration and Glx/GABA ratio scores. Middle Column: Surface-projected t-values representing linear-regression based correlations between MRS variables and high gamma-band (64– 90 Hz) power from all 104 participants (35 CON plus 69 UHR). Both Glx and ratio scores correlate positively with increased occipital gamma-band power (uncorrected), whereas GABA concentrations correlate negatively with increased gamma-band power in calcarine areas (FDR corrected), resulting in a significantly increased ratio score in the same regions. Lower Left Panel: Correlation plots for the two strongest effects in calcarine regions.

https://doi.org/10.7554/eLife.37799.010

Correlations between high gamma-band (64– 90 Hz) power and MRS estimates of Glx, GABA and Glx/GABA ratio scores were investigated using non-parametric, Monte-Carlo based (1000 permutations, independent sample regression coefficient T-statistics, alpha = 0.05, two-sided, FDR corrected) on data covering striate (calcarine fissure, cuneus, lingual gyrus) and extrastriate (superior, middle and inferior occipital gyrus) visual areas, including all available data (n = 104; 69 UHR plus 35 CON). These analyses showed that changes in Glx and Glx/GABA ratio correlated significantly (p<0.05, uncorrected) with increased right and left calcarine and right middle occipital gyrus high gamma-band power (Figure 5). In addition, increased high gamma-band power correlated significantly with decreased calcarine fissure GABA concentrations as well as with increased Glx/GABA ratio (p<0.05, FDR corrected).

Discussion

Emerging evidence suggest that circuit dysfunctions underlying the symptoms and cognitive deficits in ScZ may be caused by an alteration in E/I-balance parameters (Uhlhaas and Singer, 2012; Anticevic et al., 2012). However, direct physiological evidence for this hypothesis from non-invasive electrophysiological and neuroimaging data is so far scarce. The current study addressed this question through the investigation of resting-state gamma-band activity and MRS Glx/GABA levels, two important signatures of E/I-balance (Yizhar et al., 2011; Rowland et al., 2005), across illness stages of ScZ in MEG-data and their relationship to clinical and neuropsychological variables. Recent evidence suggests that early stage psychosis may be characterized by distinct neural signatures compared to chronic ScZ (Anticevic et al., 2015), involving a gradual shift of E/I-balance that implicates elevated glutamatergic neurotransmission at illness-onset.

Consistent with this hypothesis, we observed distinct patterns of resting-state gamma-band power in CHR-, FEP- and chronic ScZ-groups. Specifically, CHR-participants were characterized by increased gamma-band power compared to both FEP and chronic ScZ in a network including frontal and right temporal structures. FEP-patients showed largely reduced 30–90 Hz power over frontal, central and temporal areas but also showed additional increases in visual areas not observed in the chronic ScZ-group.

Importantly, the changes observed in gamma-band power across illness stages covered the entire 30–90 Hz frequency range, except for the CHR group where the increases in spectral power selectively involved the 64–90 Hz frequency band, suggesting that high gamma-band activity may constitute a marker for psychosis-risk. Overall, the pattern of spectral changes is distinct from activity associated with an oscillatory process observed during task-contexts (Hoogenboom et al., 2006; Fries et al., 2008), whereby a circumscribed modulation within a particular frequency is considered to be the hallmark of an oscillation.

The broad-band modulation observed in our data is compatible with the effects of impaired NMDA-R on PV+ cells. Carlén et al. (2012) showed that reduced NMDA-R neurotransmission on PV+ interneurons is associated with increased broad-band gamma-band power at rest while the ability to generate gamma-band oscillations after optogenetic drive of PV-interneurons was reduced. These data thus also replicate the large body of evidence for impaired generation of task-related, band-limited gamma-band oscillations in ScZ (Uhlhaas and Singer, 2010; Thuné et al., 2016), highlighting the crucial importance of impaired E/I-balance for the explanation for alterations in both resting-state as well as task-related gamma-band activity in ScZ.

Elevated excitation due to NMDA-R hypofunctioning has been implicated as a possible mechanism for the emergence of psychosis (Schobel et al., 2013) that could transiently lead to elevated high-frequency activity. This hypothesis is crucially supported by the MRS-data of Glx/GABA concentrations. Specifically, we observed that Glx-levels were elevated while GABA-concentrations were intact in CHR-participants, highlighting that psychosis-risk is intimately related to elevated glutamatergic neurotransmission. This hypothesis is consistent with previous MRS-data of elevated Glx-levels in CHR-participants (de la Fuente-Sandoval et al., 2011; Tandon et al., 2013) and findings in FEP (Kahn and Sommer, 2015).

The current findings critically extend these data by demonstrating that increased Glx-levels extend into visual cortex, which is consistent with evidence that alterations in visual perception may be indicative for transition to ScZ in CHR-participants (Klosterkötter et al., 2001). Moreover, our findings provide the first link between changes in E/I-balance parameters and fluctuations in gamma band power as increased Glx and Glx/GABA ratio correlated significantly with elevated 30-90 Hz activity.

The functional significance of the changes in resting-state gamma-band activity is underlined by the close relations with both neurocognitive and clinical parameters. During normal brain functioning, E/I-balance is fundamental for shaping information transmission of large-scale networks (Yizhar et al., 2011; Saunders et al., 2012). Consistent with this hypothesis, we observed that the degree of reductions in 30–90 Hz gamma-band power in both FEP and chronic ScZ-patients correlated with impairments in cognition and symptoms of emerging psychosis. In the CHR-group, impaired neurocognition correlated with elevated 64–90 Hz power while the presence of attenuated psychotic symptoms showed the opposite relationship. In contrast, deficits in neurocognition in FEP- and chronic ScZ-patients showed a robust correlation with reductions in gamma-band power, highlighting that disruptions in E/I-balance across illness stages in ScZ can potentially account for relations with cognitive impairments.

Robust relationships were observed between reductions in gamma-band power with age, in particular in the chronic ScZ-group. Previous MRI-data has highlighted that reduction in GM could reflect an accelerated aging process in ScZ, possibly related to outcome and medication (Schnack et al., 2016). Accordingly, one scenario is that the reductions in spectral power in chronic ScZ-patients reflect progressive pathophysiological processes that lead to circuit dysfunctions as reflected by an impaired generation of high-frequency activity and pronounced cognitive deficits. Moreover, it is conceivable that anti-psychotic medication may also contribute to the observed reductions in gamma-band power across the illness course as loss of GM has been associated with antipsychotic exposure (Ho et al., 2011) and pre-clinical evidence suggests that antipsychotic medications can reduce gamma-band oscillations (Schulz et al., 2012).

The current data have implications for the interpretation of gamma-band fluctuations and the pathophysiology of ScZ. Spontaneous changes in gamma-band power are representing a distinct aspect of electrophysiological changes typically observed during task-related scenarios that could provide important insights into circuit abnormalities. Thus, increased spiking activity at high frequencies may interfere with the generation of task-related oscillations as has been proposed previously (Hirano et al., 2015). However, we would like to note that contrary to empirical findings, this scenario likely applies primarily to early stage psychosis as chronic ScZ-patients were characterized by reduced gamma-band power.

The study has several limitations. Notably, the current conclusions are based on cross-sectional findings. Accordingly, follow-up data need to determine whether increased resting-state gamma-band power is also predictive for clinical outcomes in CHR-populations. In addition, there were differences in age- and sex-composition across clinical samples. We would like to note that the rescaling procedure employed and matched control participants for the CHR- and FEP-groups highlight that differences in gamma-band power represent the effects of different stages of psychosis. Secondly, it is currently unclear whether the trajectory of changes in spectral power could be influenced by anti-psychotic medication. However, we would like to emphasize that the large majority of CHR-participants and FEP-patients were currently not being treated with antipsychotic medication. Accordingly, it is unlikely that medication effects drove the differences at illness-onset in gamma-power.

Finally, the current study did not examine dynamic aspects of resting-state activity. There is evidence to suggest resting-state networks are not stationary. Accordingly, future studies could examine alterations in micro-states and related phenomena, such as approaches employing a Hidden Markov Model (HMM), to provide further insights into alterations of resting-state activity in ScZ (Rieger et al., 2016; Vidaurre et al., 2018).

Conclusion

The current study provides novel evidence for alterations in E/I-balance parameters in the pathophysiology of ScZ through a combination of MRS and advanced MEG. Specifically, our findings highlight that increased high gamma-band power and a shift toward increased excitation over inhibition are a hallmark of early stage psychosis and are potentially consistent with the NMDA-R hypofunctioning model of psychosis. These findings have implications for current pathophysiological theories emphasizing a shift towards increased excitation in the early stage of ScZ, with possible implications for the development of treatments and biomarkers for early detection and diagnosis. Accordingly, future studies should investigate the possibility of utilizing resting-state gamma-band power as spectral fingerprints (Siegel et al., 2012) to predict onset of psychosis as well as treatment outcomes.

Materials and methods

Participants

The following groups of participants were recruited: (1) A sample of participants meeting CHR-criteria (n = 88) from the ongoing Youth Mental Health Risk and Resilience (YouR) Study (Uhlhaas et al., 2017) and 48 matched controls (CON1) (2) A group of 21 antipsychotic-naïve ScZ patients who were experiencing their first episode of psychosis (FEP), 34 patients with chronic ScZ who were on stable antipsychotic-medication treatment and 37 matched controls (CON2). A total of n = 22 participants’ data were excluded due to excessive muscle and movement artefacts (10 CHR, 3 FEP, 4 chronic ScZ and 5 controls).

CHR-participants were recruited from NHS-services and the general population. CHR-criteria were established through the Comprehensive Assessment of At Risk Mental States (CAARMS) Interview (Yung et al., 2005) for the assessment of attenuated psychotic symptoms and the Cognitive Disturbances and Cognitive-Perceptive Basic Symptoms (COGDIS/COPER) items of the Schizophrenia Proneness Instrument, Adult version (SPI-A) (Schultze-Lutter et al., 2007). Basic symptoms describe a range of self-experienced cognitive and perceptual abnormalities that are predictive for the development of ScZ (Klosterkötter et al., 2001).

CHR-participants were excluded for current or past diagnosis with Axis I psychotic disorders, including affective psychoses, as determined by the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-IV (SCID). Other co-morbid Axis I diagnoses, such as mood or anxiety disorders, were not exclusionary and all participants were between 16 – 35 years of age (for more details, see Uhlhaas et al. (2017) and Table 1).

FEP ScZ-patients were recruited from the Department of Psychiatry and Psychotherapy, University of Cologne, and chronic ScZ patients from the Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt. Current psychopathology was examined with the Positive and Negative Symptom Scale (PANSS) (Kay et al., 1987). Control participants were screened for psychopathology with the SCID and/or the MINI-SCID interview (Sheehan et al., 1998). Neurocognition for all participant groups was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS) (Keefe et al., 2004).

The study was approved by the ethical committees of the Goethe University Frankfurt and the NHS Research Ethical Committee Glasgow and Greater Clyde. All participants provided written informed consent.

Neuroimaging

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CHR- and a matched control-group (CON1) were assessed at the Centre for Cognitive Neuroscience (CCNi), University of Glasgow. Five minutes, eyes-open resting-state was acquired using a 248-channel 4D-BTI magnetometer system (MAGNES 3600 WH, 4D-Neuroimaging, San Diego), recording at a sampling frequency of 1017.25 Hz, filtered online between DC and 400 Hz. FEP- and chronic ScZ-patients, and matched controls (CON2) were recorded at the Brain Imaging Centre (BIC), Goethe-University, Frankfurt, Germany. MEG resting-state activity was recorded with a 275-channel CTF system (Omega 2005, VSM MedTech Ltd., BC, Canada), recording at a sampling frequency of 600 Hz with a synthetic third order axial gradiometer configuration. Online filtering was applied using a 4th order Butterworth filter with 0.5 Hz high-pass and 150 Hz low-pass.

3D MPRAGE sequences were used to collect the T1-weighted data (Allegra 3Tesla scanner, BIC-Frankfurt: 160 slices, voxel size 1 mm3, FOV = 256 mm3, TR = 2300 ms, TE = 3.93 ms; Trio 3Tesla scanner, CCNi-Glasgow: 192 slices, voxel size 1 mm3, FOV = 256×256 × 176 mm3, TR = 2250 ms, TE = 2.6 ms, FA = 9°).

MEG Data Analysis

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MEG data were analysed with MATLAB using the open-source Fieldtrip Toolbox. Faulty MEG sensors (CTF data: mean (± SEM)=1 ± 0.2; 4D-BTI data: 18 ± 0.1, visually identified) expressing large signal variance or flat signals were removed from the data. For all 228 participants, the first 4 min of MEG resting-state data, available for all groups, were used in the analyses, downsampled to 400 Hz. These data were epoched into 240 non-overlapping trials of one-second duration, after first attenuating the (residual) 50 Hz line noise signal with a discrete 50 Hz Fourier transform filter. The Glasgow magnetometer data was additionally denoised offline relative to available MEG reference channel signals. Artifact-free data were created by removing trials with excessive transient muscle activity, slow drift or SQUID jumps using visual inspection, followed by ICA-based removal of eye-blink, eye-movement and ECG artifacts. This resulted in 215 ± 2.6 trials for FEP-patients, 215 ± 2.0 trials for chronic ScZ-patients, 218 ± 1.6 trials for CON2-, 220 ± 0.7 trials for CHR-, and 219 ± 1.1 trials for CON1-groups.

Whole-brain source gamma-band power (FFT data between 30 – 90 Hz, hanning tapered) was estimated using the Dynamic Imaging of Coherence Sources (DICS) beamforming approach (Gross et al., 2001) on a 5 mm grid based on the MNI template brain. We differentiated between a low (30 – 46 Hz) and high (64 – 90 Hz) gamma-band to avoid contamination of line-noise artifacts around 50/60 Hz, and because of evidence that low and high-frequency bands have distinct generating mechanisms and functional roles (Veit et al., 2017; Oke et al., 2010; van der Meer and Redish, 2009).

Prior to source estimation as well as FFT computations, data were rescaled separately per trial and channel to values between 0 and 1 (formula: X(t) – minamp/(maxamp-minamp), with X(t) representing raw amplitude at time t, and minamp/maxamp estimated across time). Our tests showed that this linear rescaling procedure was robust against changes in topographic distribution of activity (including source estimations) and spectral power shifts. The procedure was applied to correct for (1) higher variance in overall brain activity levels in the FEP, ScZ patients and, to some extend also in the CHR participants, compared to healthy controls, and (2) MEG-system differences in global activity levels and sensor types (CTF gradiometers vs. 4D-BTI magnetometers).

1H-MRS Data Acquisition

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MRS data were acquired on a Siemens Trio 3Tesla scanner and only for the CHR-group and their respective controls. The 3D MPRAGE anatomical images were first resliced into axial and coronal views to allow more precise and consistent placement of a single 2 × 2 × 2 cm3 voxel, using all three planar views, in the right middle occipital gyrus, about 1 cm to the right of the calcarine fissure and aligned within a few millimeters from the edge of voxel (see Figure 5). FASTMAP (Gruetter and Tkác, 2000) shimming of the voxel was used to improve local-field homogeneity in the area of interest. Three scans were acquired, including a full spectrum acquisition, a GABA-edited MEGA-PRESS (WIP: VB-17A) scan (128 trials), and an unsuppressed water scan (64 trials). For the current study, the last two scans were used to quantify GABA and co-edited combined Glutamate/Glutamine (Glx) concentrations. MEGA-PRESS scanning parameters included: TR/TE = 1500/68 ms, 1.9 ppm ON- and 1.5 ppm OFF-resonance editing pulse frequencies (i.e., symmetric editing to suppress macromolecule contribution), 44 Hz editing Gaussian pulse bandwidth, delta frequency of −1.7 ppm relative to water, 50 Hz water suppression, 90° flip angle, acquisition bandwidth of 1200 Hz, duration 426 ms, number of points 512.

Post-Processing of MR Spectroscopy Data

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Metabolite quantification of the MEGA-PRESS difference spectra was performed using the Matlab Toolbox Gannet 2.1 (Edden et al., 2014). Gannet-guided post-processing steps included combination of phased array coil data, time-domain frequency-and-phase correction using spectral correction, exponential line broadening, Fast Fourier Transformation (FFT), averaging, frequency and phase correction based upon fitting of the Choline and Creatine (Cr) signals, pairwise rejection of data for which fitting parameters were greater than three standard deviations from the mean, and finally subtraction to generate the edited difference spectrum.

For quantification of our metabolites of interest - GABA at 3 ppm and the co-edited Glx at 3.75 ppm - the area under the peak of GABA, Cr, and unsuppressed water (3 ppm), as well as Glx (at 3.75 ppm) were estimated, using a nonlinear fit procedure with a single Gaussian superimposed on a linear baseline. To account for individual differences in amounts of voxel gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) fractions, GABA concentrations were adjusted for CSF contamination (contamination was on average ~1%). GABA concentrations were additionally corrected for the differences in water relaxation times of the different tissue types within the voxel. Finally, both GABA and Glx concentrations were expressed as a ratio score. Water (H2O) concentration (unsuppressed) was used as reference.

Statistical Analysis of MR Spectroscopy Data

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The computed GABA/H2O, Glx/H2O concentrations, and RATIO scores (Glx/GABA) for each CHR and CON1 participant were submitted to a one-way repeated-measures ANOVA to determine group differences in metabolite concentration and/or E/I balance, using 1000 sample bootstrapping, a confidence interval of 95%, and Welch t-tests as a more robust test of equality of means for our unequal sample sized data. Results were corrected for multiple comparisons using Least Square Difference (LSD).

Statistical Analysis MEG, Demographical and Clinical Data

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Group differences in whole brain gamma-band power were evaluated by a non-parametric Monte-Carlo permutation statistics (using 2000 permutations) in combination with independent t-tests and additional False Discovery Rate (FDR) correction for multiple comparisons. Significance was assumed for p-values<0.05. Finally, demographic and clinical variables were assessed with an independent sample Kruskal-Wallis tests, alpha-level 0.05 (two-sided), adjusted for ties. BACS data were standardized (z-transformed) to a normative database, correcting for age and gender (Keefe et al., 2008). Main GROUP effects for BACS data were followed up by pairwise comparisons, corrected for multiple comparisons using Least Square Differences (LSD).

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Decision letter

  1. Richard B Ivry
    Senior and Reviewing Editor; University of California, Berkeley, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Resting-State Gamma-Band Power Alterations in Schizophrenia Reveal E/I-Balance Abnormalities Across Illness-Stages" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Sabine Kastner as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In their manuscript 'Resting-State Gamma-Band Power Alterations in Schizophrenia Reveal Functional E/I Balance Abnormalities Across Illness-Stages', the authors describe changes in gamma-band MEG power and magnetic resonance spectroscopy measures of glu and gaba across the course of illness. Specifically, the manuscript addresses at risk, first episode psychosis and chronic patients. The study is an exemplar in terms of cross-sectional analysis of schizophrenia – where the locus of disease is likely very important for understanding its origins and symptoms. The methodologies are sound – though some of the neurobiology requires clarification. The results are interesting and well presented. The sample size is the largest of its kind. The combined MRS and MEG data analysis is an excellent way to address. The study is building on top of the group's past work on altered gamma oscillation power in schizophrenia, with two extensions: 1) resting-state was used instead of task (but the significance of this is unclear; i.e., patterns of results concerning gamma-power may be similar between task and rest); 2) stage of illnesses was investigated in a more fine-grained manner compared to previous studies. The authors interpret the results as being consistent with a recent framework proposed based on resting-state fMRI by John Krystal, Alan Anticevic and others. This framework suggests that early psychosis can be modeled by ketamine, which is an NMDAR-antagonist, and moreover such effect is stronger on inhibitory neurons than excitatory neurons.

Essential revisions:

Conceptual:

The authors suggest "One central prediction for a shift in E/I-balance in ScZ towards increased excitability-levels is an increase in spontaneous gamma-band activity" and "transient increases in excitability are associated with enhanced occurrence of gamma-band power." However, given that gamma oscillations are thought to arise from the interaction between E and I cells, with the time constant of inhibitory neurons playing an important role (Nancy Kopell's work), it's not clear why enhanced excitability alone should produce increased gamma-power. Moreover, Jess Cardin's work (with Chris Moore) shows that optogenetic excitation of pyramidal neurons increases broadband power, while optogenetic excitation of PV interneurons increases gamma oscillations.

Mechanisms for broadband gamma and gamma oscillations are different, and the authors do not differentiate between them in the empirical data analyses, making it difficult to interpret the results in terms of the underlying E-I balance.

Regarding the MDS results, it's unclear why altered gamma-power was found across many brain networks, yet altered Glx concentration was only seen in occipital visual cortex.

Materials and methods:

Rescaling procedure and different sites for acquisition. In order to account for the two MEG systems used to acquire the data in at risk and symptomatic patients the authors state in the Materials and methods that they rescale each channel and trial data vector to have a value between zero and one. How exactly is this done? Could the authors present any effects on gamma that this rescaling has, (for a typical trial and channel with high and low variance)?

Clinical symptoms were correlated with gamma-power but the procedure for doing so is not described in the Materials and methods section. It is difficult to follow the procedure outlined in Results subsection “Correlations with Clinical Symptoms and Demographic Data” beginning 'Correlations between psychotic symptoms…observed t-values…'. What t values are compared? Is this a multivariate test or many univariate tests for age, neurocognition etc.?

Source localization of resting state. This is a difficult task given that these resting state networks are not stationary – but develop into shifting 'microstates'. I wonder if the authors had considered testing which resting state networks were active across the 5 minute scan (e.g. see work of Woolrich)? It might be that the power estimates from gamma constrained by whether they are in a network – show more robust features. It would be also interesting to see if RSNs had different occupancy times for the different patient and control groups. This sort of analysis would aid in unpacking the rather blunt assumption that averages over trials for all putative sources.

It is not clear why only the CHR group had spectroscopy, other than availability of existing data but this should be made explicit. E.g. subsection “1H-MRS data acquisition”, MRS acquired on who and n?

Results:

Age effects. The authors show that age shows widespread correlation with gamma-band activity. To account for age confounds in-group effects the authors state that they repeat the main analysis with age matched participants and report these in supplements. Would it not be better to include age as a confound from the outset? – We propose using an ANCOVA rather than t tests across groups for the group analysis.

Does the gxu/gaba ratio report the same combined effects as the individual glx and glu measures or are these different? Why perform stats on both? Are there correction here for multiple comparisons. Can the correlation with gamma-power be plotted? It is not clear if this is for only the CHR group or both the CHR and controls.

Measures of resting state low gamma in the first episode group revealed higher gamma (in occipital regions) and decreased excitation in prefrontal regions. This shifted to overall low levels in chronic patients. High gamma-band activity mirrored this effect and in addition was revealed to be higher in the at risk group – potentially revealing a precursor to the first episode state. This latter finding in the risk group was extended by a subgroup analysis that showed that those most vulnerable to conversion displayed this increased gamma phenotype more strongly. Finally these at risk groups exhibited MRS based correlations in enhanced glu/gaba ratios with gamma-band activity.

Details of the MRS results are scarce and thus the final conclusion (that an altered Glx/GABA ratio indicate changes in e/I balance in developing psychosis) is over-reaching. The Glx/GABA results need to be presented, and must be made clear that this is only in the CHR group (also make clear in abstract)

Discussion:

Given the results we would question the conclusion in the third paragraph of the Discussion, that suggest a high gamma-band activity constitute a marker for psychosis risk, given that this was only found in the CHR group. One would expect this signal to increase in FEP and Schz as a stratified approach to psychosis? On the whole the discussion does not reflect the considerable heterogeneity in the CHR population, lack of psychosis specificity and high levels of affective disturbance, emotional instability etc. There could be many alternative explanations to the CHR finding. To reach the conclusion drawn, one would need evidence of transition rates in the 64 CHR patients, which presumably is not available, but given current evidence would be predicted to be 6-10 patients at most. We request a more balanced discussion which bears these factors in mind.

Increased Glx has also been consistently found in FEP (as well as CHR, Discussion, fourth paragraph) and should be referenced- e.g. Kahn and Sommer, 2015. Medication has a significant role. Please consider the issue of medication in the Discussion in the light of this review.

https://doi.org/10.7554/eLife.37799.016

Author response

Summary:

In their manuscript 'Resting-State Gamma-Band Power Alterations in Schizophrenia Reveal Functional E/I Balance Abnormalities Across Illness-Stages', the authors describe changes in gamma-band MEG power and magnetic resonance spectroscopy measures of glu and gaba across the course of illness. Specifically, the manuscript addresses at risk, first episode psychosis and chronic patients. The study is an exemplar in terms of cross-sectional analysis of schizophrenia – where the locus of disease is likely very important for understanding its origins and symptoms. The methodologies are sound – though some of the neurobiology requires clarification. The results are interesting and well presented. The sample size is the largest of its kind. The combined MRS and MEG data analysis is an excellent way to address. The study is building on top of the group's past work on altered gamma oscillation power in schizophrenia, with two extensions: 1) resting-state was used instead of task (but the significance of this is unclear; i.e., patterns of results concerning gamma-power may be similar between task and rest); 2) stage of illnesses was investigated in a more fine-grained manner compared to previous studies. The authors interpret the results as being consistent with a recent framework proposed based on resting-state fMRI by John Krystal, Alan Anticevic and others. This framework suggests that early psychosis can be modeled by ketamine, which is an NMDAR-antagonist, and moreover such effect is stronger on inhibitory neurons than excitatory neurons.

We thank the reviewers for the overall positive assessment of our work. In regards to the significance of the alterations in resting-state gamma-band power and their relationship to changes in task-related oscillations, we would like to note that the upregulation of high-frequency activity correlated robustly with both psychopathological and neuropsychological variables across different clinical groups, suggesting that the increase in gamma-band power are functional relevant.

We would like to highlight, however, that findings by our group (Sun, Castellanos et al., 2013, Grent-'t-Jong, Rivolta et al., 2016) and others (Kwon, O'Donnell et al., 1999, Spencer, Niznikiewicz et al., 2008) show that the pattern of task-related gamma-band oscillations is different. That is, ScZ-patients as well as at-risk populations are consistently characterized by a reduction in high-frequency activity in both sensory and cognitive tasks (Thune, Recasens et al., 2016). These findings, together with our analyses into the nature of gamma-band activity (see below), therefore suggest that the alterations in resting-state activity vs. task-related neural oscillations in ScZ are potentially two distinct phenomena.

Essential revisions:

Conceptual:

The authors suggest "One central prediction for a shift in E/I-balance in ScZ towards increased excitability-levels is an increase in spontaneous gamma-band activity" and "transient increases in excitability are associated with enhanced occurrence of gamma-band power." However, given that gamma oscillations are thought to arise from the interaction between E and I cells, with the time constant of inhibitory neurons playing an important role (Nancy Kopell's work), it's not clear why enhanced excitability alone should produce increased gamma-power. Moreover, Jess Cardin's work (with Chris Moore) shows that optogenetic excitation of pyramidal neurons increases broadband power, while optogenetic excitation of PV interneurons increases gamma oscillations.

We thank the reviewers for highlighting the importance of the interactions between inhibitory interneurons and pyramidal cells. We would like to point out that we have highlighted the importance of both inhibitory interneurons and excitatory neurotransmission towards the generation of gamma-band oscillations in the introduction. However, we feel that additional importance could have been assigned to this aspect. Accordingly, we have added references to the work of Nancy Kopell (Kopell and LeMasson 1994) to highlight this point.

In regards to the interpretation of the upregulation in gamma-band power, it is important to note that activation of interneurons through optogenetic stimulation at gamma-band frequencies produces an increase in band-limited, oscillatory activity in the 40-60 Hz frequency range (see Cardin et al., 2011, Figure 3D). Our analysis of gamma-band power in the current study suggests, however, that the increases observed are broad-band in nature and accordingly unlikely of rhythmic origin (see Figure 2—figure-supplement 1). Accordingly, we feel that our findings are not compatible with a mechanism that purely involves interneuron-mediated gamma-band oscillations.

Support for the hypothesis that the current findings are due to increased excitability rather than interneuron-driven gamma-band oscillations is provided by another study by Carlen et al., 2011 which involved the manipulation of NMDA-Rs on parvalbumin-expressing interneurons (PV+). Specifically, the authors generated mice lacking NMDA-R neurotransmission in PV+ cells that were associated with a broad-band increase of gamma-band power (Carlen et al., 2001, Figure 2A) that resembles the effects observed in our study.

As the blockade of NMDA-Rs on PV-cells is one of the mechanisms implicated in the effects of Ketamine (Kopell and LeMasson 1994, Kinney, Davis et al., 2006), we feel that the increase in excitability is the most likely explanation for the findings observed in our study. This is furthermore supported by the fact that the increase in gamma-band power in the CHR-group correlated with MRS-measured Glx/GABA-ratio. Accordingly, we have revised the Discussion section to highlight this point.

Mechanisms for broadband gamma and gamma oscillations are different, and the authors do not differentiate between them in the empirical data analyses, making it difficult to interpret the results in terms of the underlying E-I balance.

We thank the reviewer for highlighting this important issue and we fully agree that it is important to distinguish between these two phenomena. To address this question, we examined all AAL nodes covering significant regions of group differences in 30-90 Hz power. These were then examined for each 5 Hz frequency bin separately. These analyses revealed that the power changes were broad-band in nature for all groups (see Figure 2—figure supplement 1).

Accordingly, we feel that these findings provide further support for the possibility that the upregulation of gamma-band power is a consequence of increased excitability of neural circuits. This is supported by the fact that a) NMDA-R hypofunction leads to an upregulation of broad-band power (Carlen et al., 2011) and b) NMDA-R antagonists increase spiking activating in principal cells due to the reduction of inhibitory transmission (Homayoun and Moghaddam 2007). This information has been added to the manuscript as a separate paragraph in the Results and Discussion sections.

Regarding the MDS results, it's unclear why altered gamma-power was found across many brain networks, yet altered Glx concentration was only seen in occipital visual cortex.

The reason for measuring Glx concentrations only in the occipital cortex is that because of the low concentration of MR detectable metabolites, acquisition of MRS-data is restricted to the analysis of small regions-of-interest (ROIs) to insure high enough SNR. Recordings across larger cortical and subcortical regions or the whole-brain would be too time consuming. Accordingly, we have selected ROIs for the acquisition of GABA/Glx levels that would be meaningful within our MEG-battery, which also includes visual tasks. We were interested in exploring the contribution of early sensory regions towards alterations in E/I-balance.

Materials and methods:

Rescaling procedure and different sites for acquisition. In order to account for the two MEG systems used to acquire the data in at risk and symptomatic patients the authors state in the Materials and methods that they rescale each channel and trial data vector to have a value between zero and one. How exactly is this done? Could the authors present any effects on gamma that this rescaling has, (for a typical trial and channel with high and low variance)?

The rescaling procedure included searching for the minimum and maximum value across timepoints within each trial and each channel separately, and subsequently subtracting the minimum value from each timepoint value within a trial and channel and dividing it by the range of values (maximum minus minimum value). This was done on artefact-cleaned single-trial data, prior to all subsequent FFT- and source-analyses.

Author response image 1 shows data from one example trial taken from the same virtual channel (left MOG: middle occipital gyrus) in a subject with high variance across trials and time (upper panels) and a subject with low variance across trials and time (lower panels). These results show that the rescaling procedure is a linear operation that does not introduce a shift in the relative power of any frequency across the entire frequency spectrum.

Author response image 1>

Author response image 1
Overview of effects of the scaling procedure on shifts in the FFT spectrum: shown are two example trials from the same virtual channel data (LMOG), but from different participants, with either high (four top panel figures) or low variance across trials (four bottom panel figures).

Left column shows the unscaled data prior to (top) and after (bottom) Fast-Fourier transformation. Right column shows the same data, but rescaled, using the minimum and maximum values within that trial across time (formula: X(t) – min/ (max-min), with X(t) representing the amplitude at time t). These comparisons show that the rescaling procedure does not change the shape of the powerspectrum.

https://doi.org/10.7554/eLife.37799.013

Clinical symptoms were correlated with gamma-power but the procedure for doing so is not described in the Materials and methods section. It is difficult to follow the procedure outlined in Results subsection “Correlations with Clinical Symptoms and Demographic Data” beginning 'Correlations between psychotic symptoms…observed t-values…'. What t values are compared? Is this a multivariate test or many univariate tests for age, neurocognition etc.?

We agree that both description and rationale for the correlational analyses were not very clear. This has now been corrected. The text now reads: “We also systematically explored relationships between gamma-band power and demographic data (age, sex), psychopathology (total CAARMS, total PANNS) and neurocognitive (composite BACS scores) variables, given recently reported strong covariation of symptoms, age and sex on neuroimaging phenotypes and the need to incorporate them in evaluating patient data (Moser, Doucet et al. 2018).”

Our goal was to determine how each factor influenced findings across the regions of significant gamma-band changes between patients and controls. Thus, rather than estimating linear correlations between group differences in source power and variable of interests or regressing out contributions of these variables, we used them directly as a covariate and repeated our original statistical group analyses. This approach was expected to most optimally highlight regional differences in sensitivity to each individual covariate, as the data was now permuted across the covariate data rather than across gamma-power data from all subjects. The logic here is that any covariate that has no correlation with the main group effect in gamma-power, such as handedness, will result in a lack of significant group differences, whereas any interaction with the covariate will result in a significant finding, representing GROUP by control variable interactions.

Source localization of resting state. This is a difficult task given that these resting state networks are not stationary – but develop into shifting 'microstates'. I wonder if the authors had considered testing which resting state networks were active across the 5 minute scan (e.g. see work of Woolrich)? It might be that the power estimates from gamma constrained by whether they are in a network – show more robust features. It would be also interesting to see if RSNs had different occupancy times for the different patient and control groups. This sort of analysis would aid in unpacking the rather blunt assumption that averages over trials for all putative sources.

We thank the reviewers for pointing out this interesting suggestion. However, we feel that the analyses of microstates are beyond the scope of the paper. While it is correct that averaging activity of resting-state data may ignore dynamic aspects of network-activity, we feel that the present analysis went already significantly beyond the current state-of-the-art in the field by providing a comprehensive analysis of gamma-band activity across the entire frequency range and source-space.

In addition, we would like to point out that our main interest was primarily in potential changes in E/I balance in schizophrenia patients across different stages of the disorder, based on predicted PV+ interneuron hypofunction, which are less likely to be driven by state-changes. These changes were also expected to be revealed by power differences in much higher frequencies (above 50 Hz) than those reported using microstate analysis (< 45 Hz; e.g. Vidaurre, Quinn et al., 2016). Therefore, we believe that applying such microstates analyses to our data was not suited to our main research question.

We do acknowledge, however, that microstates and related phenomena, such as approaches employing a Hidden Markov Model (HMM), provide further exciting opportunities to investigate alterations in resting-state activity in ScZ (Rieger, Diaz Hernandez et al., 2016, Vidaurre, Abeysuriya et al., 2017). Accordingly, we have added a section to the Discussion where we highlight the limitation of our analysis as well as opportunities for further research using this approach.”

It is not clear why only the CHR group had spectroscopy, other than availability of existing data but this should be made explicit. E.g. subsection “1H-MRS data acquisition”, MRS acquired on who and n?

The reason for the acquisition of MRS-data in the CHR-group only is that the protocol for measuring GABA/Glx levels was only introduced at the Glasgow-site where MEG-data from CHR-participants were acquired. In the revised version of the manuscript, we have made this fact more explicit.

Results:

Age effects. The authors show that age shows widespread correlation with gamma-band activity. To account for age confounds in-group effects the authors state that they repeat the main analysis with age matched participants and report these in supplements. Would it not be better to include age as a confound from the outset? – We propose using an ANCOVA rather than t tests across groups for the group analysis.

As highlighted in the revised Results section, we do not regard age, or any of the other variables used in the correlations, as pure confounds that have to be controlled for. Rather, we see them as essential elements of variations between individuals that determine their phenotype and thereby influence group differences. We included the age-matched analysis to demonstrate that even with age differences controlled for, there are still similar changes seen in gamma-power differences between the groups. In other words, an ANCOVA would not have removed the ‘confound’, as it is not a confound in the first place, but rather a relevant feature of the phenotype differences. Our approach of using age as a covariate brings out novel information on regional profiles of group differences in gamma-power.”

Does the gxu/gaba ratio report the same combined effects as the individual glx and glu measures or are these different? Why perform stats on both? Are there correction here for multiple comparisons. Can the correlation with gamma-power be plotted? It is not clear if this is for only the CHR group or both the CHR and controls.

Glx/GABA ratio reports the combined effects of each of the metabolites for each individual. We feel that this analysis is informative as it effectively examines alterations in E/I-balance which is not addressed by analysis of GABA or Glx-levels alone. For example, elevated Glx and GABA levels in CHR-participants would only show that both metabolites are upregulated but not whether there is a shift in the ratio between them which is a critical test for the E/I-balance hypothesis. We also would like to point out that the reported statistical results were corrected for multiple comparisons using Least Square Differences.

We agree with the reviewers on the more general point that the correlations between MRS measures and gamma-band power were not optimally presented. Accordingly, we have re-analysed our data (see new Figure 5). Specifically, we have changed the main statistical approach through using a linear-regression-based estimation of correlations between the three MRS measures (Glx, GABA, ratio) and visual cortex gamma-band power in the calcarine fissure, cuneus, lingual gyrus, and superior, middle and inferior occipital gyri. This approach improved the visualization of the correlations as well as further strengthen our interpretation that changes in E/I balance in the direction of increased excitation are reflected in increased RS gamma-power.”

Measures of resting state low gamma in the first episode group revealed higher gamma (in occipital regions) and decreased excitation in prefrontal regions. This shifted to overall low levels in chronic patients. High gamma-band activity mirrored this effect and in addition was revealed to be higher in the at risk group – potentially revealing a precursor to the first episode state. This latter finding in the risk group was extended by a subgroup analysis that showed that those most vulnerable to conversion displayed this increased gamma phenotype more strongly. Finally these at risk groups exhibited MRS based correlations in enhanced glu/gaba ratios with gamma-band activity.

This is an excellent summary of the main findings.

Details of the MRS results are scarce and thus the final conclusion (that an altered Glx/GABA ratio indicate changes in e/I balance in developing psychosis) is over-reaching. The Glx/GABA results need to be presented, and must be made clear that this is only in the CHR group (also make clear in abstract)

We thank the reviewers for pointing out this limitation. We have highlighted in the Abstract that MRS-data were only obtained in the CHR-group. Moreover, we have changed the final sentence in the Abstract in the following way: The current study suggests that resting-state gamma-power and altered Glx/GABA ratio indicate changes in E/I-balance parameters across illness stages that could underlie the development of psychosis.”

Discussion:

Given the results we would question the conclusion in the third paragraph of the Discussion, that suggest a high gamma-band activity constitute a marker for psychosis risk, given that this was only found in the CHR group. One would expect this signal to increase in FEP and Schz as a stratified approach to psychosis? On the whole the discussion does not reflect the considerable heterogeneity in the CHR population, lack of psychosis specificity and high levels of affective disturbance, emotional instability etc. There could be many alternative explanations to the CHR finding. To reach the conclusion drawn, one would need evidence of transition rates in the 64 CHR patients, which presumably is not available, but given current evidence would be predicted to be 6-10 patients at most. We request a more balanced discussion which bears these factors in mind.

We thank the reviewers for these critical and informative observations. In regards to the question whether one would expect the gamma-band signal to increase in FEP and chronic ScZ, we would like to note that current pathophysiological theories have proposed that there are distinct signatures during different stages of psychosis. Specifically, there is evidence to suggest that participants at clinical high-risk (CHR) and first-episode psychosis (FEP) are characterized by increased connectivity and metabolism while the opposite pattern is observed in patients with chronic ScZ (Schobel, Chaudhury et al., 2013, Anticevic, Corlett et al., 2015). Our findings support this framework and the non-linear trajectory of circuit impairments by identifying distinct patterns of gamma-band activity in CHR, FEP and chronic ScZ.

In regards to the heterogeneity of CHR-populations, the reviewer is correct in highlighting this issue. We have tried to address this point through stratifying the CHR-group according to distinct CHR-criteria. Importantly, the CHR-group which met both basic symptom (BS) and ultra high-risk criteria (UHR) was characterized by the most pronounced upregulation of gamma-band activity compared to the groups which met only BS and UHR-criteria. This finding is important as the combination of UHR and BS criteria significantly elevated psychosis risk (Schultze-Lutter, Klosterkotter et al., 2014). Accordingly, our findings provide further evidence that the upregulation of gamma-band activity is more specifically related to CHR-state and not other variables, such as affective disturbances and emotional instability.

In regards to the transition rates, we would like to note that we are still in the process of following-up the current sample of CHR-participants. Given that the mean follow-up period is currently only 1 year, we feel that a longer follow-up period is required. In the revision of the Discussion, we have tried to address some of the issues raised by the reviewers and provided a more balanced discussion addressing issues of heterogeneity and trajectory. Specifically, we have added the following sentence to the Discussion section: “Accordingly, follow-up data need to determine whether increased resting-state, gamma-band power is also predictive for clinical outcomes in CHR-populations.”

Increased Glx has also been consistently found in FEP (as well as CHR, Discussion, fourth paragraph) and should be referenced- e.g. Kahn and Sommer, 2015. Medication has a significant role. Please consider the issue of medication in the Discussion in the light of this review.

We thank the reviewers for pointing out these findings. We have updated the relevant sections of the manuscript and added the reference. In regards to the issue of the influence of anti-psychotic medication on our findings, the pattern of gamma-band activity in both CHR- and FEP-participants is unlikely due to the impact of medication as the majority of participants in these groups were unmedicated. However, it is conceivable that the downregulation of high-frequency activity in the chronic ScZ-group is possibly related to the impact of anti-psychotic medication which we acknowledged in the discussion of our findings (Discussion, eighth paragraph). We feel that this is an important area for further study, in particular in regards to the fact that the prolonged consequence of antipsychotics on gamma-band activity are largely unknown. Preliminary data suggest that antipsychotic medications could potentially negatively impact high-frequency activity (Anderson, Pinault et al., 2014).

https://doi.org/10.7554/eLife.37799.017

Article and author information

Author details

  1. Tineke Grent-'t-Jong

    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Conceptualization, Software, Formal analysis, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3177-5346
  2. Joachim Gross

    1. Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
    2. Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
    Contribution
    Formal analysis, Funding acquisition, Writing—original draft
    Competing interests
    No competing interests declared
  3. Jozien Goense

    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Formal analysis, Writing—original draft
    Competing interests
    No competing interests declared
  4. Michael Wibral

    MEG-Unit, Goethe University, Frankfurt am Main, Germany
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8010-5862
  5. Ruchika Gajwani

    Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Project administration
    Competing interests
    No competing interests declared
  6. Andrew I Gumley

    Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Funding acquisition
    Competing interests
    No competing interests declared
  7. Stephen M Lawrie

    Department of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
    Contribution
    Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2444-5675
  8. Matthias Schwannauer

    Department of Clinical Psychology, University Edinburgh, Edinburgh, United Kingdom
    Contribution
    Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4683-2596
  9. Frauke Schultze-Lutter

    1. University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
    2. Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  10. Tobias Navarro Schröder

    Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway
    Contribution
    Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6498-1846
  11. Dagmar Koethe

    1. Department of Psychosomatics and Psychotherapeutic Medicine, Central Institute of Mental health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
    2. Brain and Mind Centre, University of Sydney, Sydney, Australia
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  12. F Markus Leweke

    1. Department of Psychosomatics and Psychotherapeutic Medicine, Central Institute of Mental health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
    2. Brain and Mind Centre, University of Sydney, Sydney, Australia
    3. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8163-195X
  13. Wolf Singer

    1. Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
    2. Ernst Strüngmann Institute for Neuroscience and the Max Planck Society, Frankfurt am Main, Germany
    3. Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
    Contribution
    Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8299-2319
  14. Peter J Uhlhaas

    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    peter.uhlhaas@glasgow.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0892-2224

Funding

Medical Research Council (MR/L011689/1)

  • Peter Uhlhaas

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Dr. Uhlhaas has received research support from Lilly and Lundbeck. The study was supported by the Medical Research Council (MR/L011689/1). We thank Hanna Thune, Christine Gruetzner, Davide Rivolta and Frances Crabbe for help in the acquisition of MEG/MRI/MRS-data. The investigators also acknowledge the support of the Scottish Mental Health Research Network (http://www.smhrn.org.uk) now called the NHS Research Scotland Mental Health Network (NRS MHN: http://www.nhsresearchscotland.org.uk/research-areas/mental-health) for providing assistance with participant recruitment, interviews, and cognitive assessments. We would like to thank both the participants and patients who took part in the study and the research assistants of the YouR-study for supporting the recruitment and assessment of CHR-participants.

Ethics

Human subjects: The study was approved by the ethical committees of the Goethe University Frankfurt and the NHS Research Ethical Committee Glasgow & Greater Clyde. All participants provided written informed consent.

Senior and Reviewing Editor

  1. Richard B Ivry, University of California, Berkeley, United States

Publication history

  1. Received: April 23, 2018
  2. Accepted: August 30, 2018
  3. Version of Record published: September 27, 2018 (version 1)

Copyright

© 2018, Grent-'t-Jong et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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