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. 2020 Aug 1;77(8):852-862.
doi: 10.1001/jamapsychiatry.2020.0284.

Association of Magnetoencephalographically Measured High-Frequency Oscillations in Visual Cortex With Circuit Dysfunctions in Local and Large-scale Networks During Emerging Psychosis

Tineke Grent-'t-Jong  1   2 , Ruchika Gajwani  3 , Joachim Gross  1   4 , Andrew I Gumley  3 , Rajeev Krishnadas  1 , Stephen M Lawrie  5 , Matthias Schwannauer  6 , Frauke Schultze-Lutter  7 , Peter J Uhlhaas  1   2
Affiliations

Association of Magnetoencephalographically Measured High-Frequency Oscillations in Visual Cortex With Circuit Dysfunctions in Local and Large-scale Networks During Emerging Psychosis

Tineke Grent-'t-Jong et al. JAMA Psychiatry. .

Erratum in

  • Update to Open Access Status.
    [No authors listed] [No authors listed] JAMA Psychiatry. 2020 Jun 1;77(6):652. doi: 10.1001/jamapsychiatry.2020.1139. JAMA Psychiatry. 2020. PMID: 32374356 Free PMC article. No abstract available.

Abstract

Importance: Psychotic disorders are characterized by impairments in neural oscillations, but the nature of the deficit, the trajectory across illness stages, and functional relevance remain unclear.

Objectives: To examine whether changes in spectral power, phase locking, and functional connectivity in visual cortex are present during emerging psychosis and whether these abnormalities are associated with clinical outcomes.

Design, setting, and participants: In this cross-sectional study, participants meeting clinical high-risk criteria for psychosis, participants with first-episode psychosis, participants with affective disorders and substance abuse, and a group of control participants were recruited. Participants underwent measurements with magnetoencephalography and magnetic resonance imaging. Data analysis was carried out between 2018 and 2019.

Main outcomes and measures: Magnetoencephalographical activity was examined in the 1- to 90-Hz frequency range in combination with source reconstruction during a visual grating task. Event-related fields, power modulation, intertrial phase consistency, and connectivity measures in visual and frontal cortices were associated with neuropsychological scores, psychosocial functioning, and clinical symptoms as well as persistence of subthreshold psychotic symptoms at 12 months.

Results: The study participants included those meeting clinical high-risk criteria for psychosis (n = 119; mean [SD] age, 22 [4.4] years; 32 men), 26 patients with first-episode psychosis (mean [SD] age, 24 [4.2] years; 16 men), 38 participants with affective disorders and substance abuse (mean [SD] age, 23 [4.7] years; 11 men), and 49 control participants (mean age [SD], 23 [3.6] years; 16 men). Clinical high-risk participants and patients with first-episode psychosis were characterized by reduced phase consistency of β/γ-band oscillations in visual cortex (d = 0.63/d = 0.93). Moreover, the first-episode psychosis group was also characterized by reduced occipital γ-band power (d = 1.14) and altered visual cortex connectivity (d = 0.74-0.84). Impaired fronto-occipital connectivity was present in both clinical high-risk participants (d = 0.54) and patients with first-episode psychosis (d = 0.84). Importantly, reductions in intertrial phase coherence predicted persistence of subthreshold psychosis in clinical high-risk participants (receiver operating characteristic area under curve = 0.728; 95% CI, 0.612-0.841; P = .001).

Conclusions and relevance: High-frequency oscillations are impaired in the visual cortex during emerging psychosis and may be linked to behavioral and clinical impairments. Impaired phase consistency of γ-band oscillations was also associated with the persistence of subthreshold psychosis, suggesting that magnetoencephalographical measured neural oscillations could constitute a biomarker for clinical staging of emerging psychosis.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Krishnadas reported grants from Neurosciences Foundation and NHSGGC R and D during the conduct of the study. Dr Lawrie reported grants and personal fees from Janssen, nonfinancial support from Otsuka, grants from Lundbeck, and personal fees from Sunovion outside the submitted work. Dr Uhlhaas reported research support from Lilly and Lundbeck outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Paradigm and Task Performance
A, Inward moving grating task: participants report, by button press, the onset of a change in velocity of inward motion of the visual stimulus (correct response window, 200-1200 milliseconds). Feedback on performance was provided on every trial, shortly after the response onset terminated stimulus presentation. B, Histograms of group means and standard errors for accuracy (% correct), mean reaction times (RTs), and behavioral variability (intraindividual standard deviation of RTs). CHR-N indicates clinical high risk negative; CHR-P, clinical high risk positive; FEP, first-episode psychosis; HC, healthy control individuals. aIndicates significant group differences (Welch F tests, α = .05, 2-sided, 1000 samples bootstrapping, Games-Howell corrected for multiple comparisons).
Figure 2.
Figure 2.. Sensor and Source-Power Magnetoencephalography (MEG) Data
A, Sensor-level MEG data: left topographical distribution plot shows grand average–induced γ power (n = 232) changes from baseline, with white dots marking the sensors for which the time frequency response (TFR) plot in the middle is plotted. In the TFR plot, the outlined (black box) window indicates the window of statistical testing for group differences in γ power. The TFR plot shows evoked activity from stimulus onset (time zero) to approximately 250 milliseconds, from which latency-induced activity is shown up to 1500 milliseconds. Right topographical distribution plot shows F values of significant (marked with white dots) sensors showing a main group effect on γ power. B, Beamformer-localized main group effect on γ power. Lighter-blue and light-orange values mark areas of significant effects uncorrected for multiple comparisons, whereas the darker colors display false discovery rate–corrected areas (α = .05; 2-sided). C, Locations across visual cortical areas of significant γ activity from which virtual channel time-series MEG data were reconstructed. The 10 occipital regions of interest (ROIs) included 3 subregions covering the primary visual cortex. CAL indicates calcarine; CUN, cuneus; IOG, inferior occipital gyrus; MOG, middle occipital gyrus; SOG, superior occipital gyrus.
Figure 3.
Figure 3.. Virtual Channel Time Frequency Response (TFR) and Intertrial Phase Coherence (ITPC) Analyses
A, Top 4 panels show per group the TFR, averaged over all virtual-channel regions of interest (ROIs) shown in Figure 2C. Bottom right panels: TFR plot with statistical results (nonparametric, Monte Carlo–based permutation independent t tests) of group differences in time-frequency clusters between 0 and 750 milliseconds, with significant clusters outlined and the remaining nonsignificant time-frequency bins masked out (opacity, 0.45). The line graph on the left shows the γ (57-67 Hz) response over time per group, with error bars representing standard error of the mean. B, Top panels show the ITPC responses per group and bottom panels show the significant group differences (between 0-350 milliseconds), with significant time-frequency bins outlined. The line graph on the left shows γ (30-50 Hz) range ITPC responses per group, with error bars representing standard error of the mean. CHR-N indicates clinical high risk negative; CHR-P, clinical high risk positive; FEP, first-episode psychosis; HC, healthy control individuals.
Figure 4.
Figure 4.. Receiver Operating Characteristic (ROC) Curve Analysis and Granger Causality (GC) Functional Connectivity
A, On the right, ROC curve computed from prediction probabilities associated with a significant logistic regression model for predicting 12 months Comprehensive Assessment of At Risk Mental States (CAARMS; attenuated psychotic symptoms) persistence status from baseline magnetoencephalography (MEG) recordings of intertrial phase coherence (ITPC) of visual cortex responses (left and right cuneus and left middle occipital gyrus; locations shown in left panel). B, Results of cluster-based statistics on GC data showing range of significant effects in first-episode psychosis (FEP), clinical high-risk positive (CHR-P), and clinical high risk negative (CHR-N) groups, compared with healthy control individuals (HC). The main connections tested are plotted on a smoothed surface of a standard Montreal Neurological Institute brain, with red lines representing increased and blue lines decreased GC values, compared with HC. For each significant connection, GC values are plotted across the frequency spectrum, separately per group (with error bars indicating standard error of the mean), and a horizontal line indicating the frequency range of significant group effects. The GC was computed for data between 250 to 750 milliseconds after stimulus onset. The directed asymmetry indices were all positive in the significant contrasts, indicating feed-forward flow of information between the nodes. AUC indicates area under curve.
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References

    1. Gray CM, König P, Engel AK, Singer W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature. 1989;338(6213):334-337. doi: 10.1038/338334a0 - DOI - PubMed
    1. Womelsdorf T, Fries P, Mitra PP, Desimone R. Gamma-band synchronization in visual cortex predicts speed of change detection. Nature. 2006;439(7077):733-736. doi: 10.1038/nature04258 - DOI - PubMed
    1. Kim H, Ährlund-Richter S, Wang X, Deisseroth K, Carlén M. Prefrontal parvalbumin neurons in control of attention. Cell. 2016;164(1-2):208-218. doi: 10.1016/j.cell.2015.11.038 - DOI - PMC - PubMed
    1. Uhlhaas PJ, Singer W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron. 2006;52(1):155-168. doi: 10.1016/j.neuron.2006.09.020 - DOI - PubMed
    1. Spencer KM, Nestor PG, Niznikiewicz MA, Salisbury DF, Shenton ME, McCarley RW. Abnormal neural synchrony in schizophrenia. J Neurosci. 2003;23(19):7407-7411. doi: 10.1523/JNEUROSCI.23-19-07407.2003 - DOI - PMC - PubMed

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