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Oscillatory Neural Signatures of Visual Perception Across Developmental Stages in Individuals With 22q11.2 Deletion Syndrome

  • Valentina Mancini
    Correspondence
    Address correspondence to Valentina Mancini, M.D.
    Affiliations
    Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
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  • Vincent Rochas
    Affiliations
    Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland

    Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland
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  • Martin Seeber
    Affiliations
    Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
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  • Tineke Grent-‘t-Jong
    Affiliations
    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland

    Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
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  • Tonia A. Rihs
    Affiliations
    Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
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  • Caren Latrèche
    Affiliations
    Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
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  • Peter J. Uhlhaas
    Affiliations
    Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland

    Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
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  • Author Footnotes
    1 CMM and SE contributed equally to this work as supervisors.
    Christoph M. Michel
    Footnotes
    1 CMM and SE contributed equally to this work as supervisors.
    Affiliations
    Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland

    Center for Biomedical Imaging, Lausanne, Switzerland
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  • Author Footnotes
    1 CMM and SE contributed equally to this work as supervisors.
    Stephan Eliez
    Footnotes
    1 CMM and SE contributed equally to this work as supervisors.
    Affiliations
    Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland

    Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
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  • Author Footnotes
    1 CMM and SE contributed equally to this work as supervisors.
Open AccessPublished:March 06, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.02.961

      Abstract

      Background

      Numerous behavioral studies have highlighted the contribution of visual perceptual deficits to the nonverbal cognitive profile of individuals with 22q11.2 deletion syndrome. However, the neurobiological processes underlying these widespread behavioral alterations are yet to be fully understood. Thus, in this paper, we investigated the role of neural oscillations toward visuoperceptual deficits to elucidate the neurobiology of sensory impairments in deletion carriers.

      Methods

      We acquired 125 high-density electroencephalography recordings during a visual grating task in a group of 62 deletion carriers and 63 control subjects. Stimulus-elicited oscillatory responses were analyzed with 1) time-frequency analysis using wavelets decomposition at sensor and source level, 2) intertrial phase coherence, and 3) Granger causality connectivity in source space. Additional analyses examined the development of neural oscillations across age bins.

      Results

      Deletion carriers had decreased theta-band (4–8 Hz) and gamma-band (58–68 Hz) spectral power compared with control subjects in response to the visual stimuli, with an absence of age-related increase of theta- and gamma-band responses. Moreover, adult deletion carriers had decreased gamma- and theta-band responses but increased alpha/beta desynchronization (10–25 Hz) that correlated with behavioral performance. Granger causality estimates reflected an increased frontal-occipital connectivity in the beta range (22–40 Hz).

      Conclusions

      Deletion carriers exhibited decreased theta- and gamma-band responses to visual stimuli, while alpha/beta desynchronization was preserved. Overall, the lack of age-related changes in deletion carriers implicates developmental impairments in circuit mechanisms underlying neural oscillations. The dissociation between the maturation of theta/gamma- and alpha/beta-band responses may indicate a selective impairment in supragranular cortical layers, leading to compensatory top-down connectivity.

      Keywords

      Prominent disruptions of brain oscillations, particularly in the gamma-band range, have been observed in patients with schizophrenia and during earlier stages of the disease in patients with a first episode of psychosis and individuals at clinical high risk for psychosis (
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      ). Behavioral, neuroimaging, and genetic findings highlighted a shared neurobiological vulnerability between 22q11DS and idiopathic psychosis (
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      ). For instance, 22q11DS is characterized by impaired visuospatial processing (
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      • Moss E.
      • McDonald-McGinn D.
      • Zackai E.
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      The neurocognitive phenotype of the 22q11.2 deletion syndrome: Selective deficit in visual-spatial memory.
      ) that encompasses deficits in the discrimination of local details and selective deficits in visuospatial memory (
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      Visual perception and processing in children with 22q11.2 deletion syndrome: Associations with social cognition measures of face identity and emotion recognition.
      ,
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      • Amato M.
      • Cabaral M.H.
      • Cruz J.
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      Quantifying the resolution of spatial and temporal representation in children with 22q11.2 deletion syndrome.
      ,
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      Visual memory profile in 22q11.2 microdeletion syndrome: Are there differences in performance and neurobiological substrates between tasks linked to ventral and dorsal visual brain structures? A cross-sectional and longitudinal study.
      ), which could reflect findings of reduced activation in ventral and dorsal streams (
      • Magnée M.J.C.M.
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      Proline and COMT Status affect visual connectivity in children with 22q11.2 deletion syndrome.
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      • Schneider M.
      • et al.
      Visual processing deficits in 22q11.2 deletion syndrome.
      ). Consistently, studies in mice with the homologous deletion were characterized by deficits in gamma- and theta-band oscillations in V1 (
      • Hamm J.P.
      • Peterka D.S.
      • Gogos J.A.
      • Yuste R.
      Altered cortical ensembles in mouse models of schizophrenia.
      ).
      Risk genes such as DGCR8, PRODH, CXCR4, and ZDHHC8 have been implicated in axonal growth and glutamatergic and GABAergic (gamma-aminobutyric acidergic) neural transmission (
      • Motahari Z.
      • Moody S.A.
      • Maynard T.M.
      • Lamantia A.S.
      In the line-up: Deleted genes associated with DiGeorge/22q11.2 deletion syndrome: Are they all suspects?.
      ,
      • Meechan D.W.
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      Diminished dosage of 22q11 genes disrupts neurogenesis and cortical development in a mouse model of 22q11 deletion/DiGeorge syndrome.
      ,
      • Meechan D.W.
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      Cxcr4 regulation of interneuron migration is disrupted in 22q11.2 deletion syndrome.
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      Deficits in microRNA-mediated Cxcr4/Cxcl12 signaling in neurodevelopmental deficits in a 22q11 deletion syndrome mouse model.
      ,
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      • Han J.
      • Kavanagh D.H.
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      Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia.
      ), which are important for the generation of gamma-band oscillations (
      • Cardin J.A.
      • Carlén M.
      • Meletis K.
      • Knoblich U.
      • Zhang F.
      • Deisseroth K.
      • et al.
      Driving fast-spiking cells induces gamma rhythm and controls sensory responses.
      ). In line with this evidence, previous studies in human deletion carriers have identified deficient gamma-band response during auditory processing (
      • Larsen K.M.
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      • Birknow M.R.
      • Kjær T.N.
      • Baaré W.F.C.
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      22q11.2 deletion syndrome is associated with impaired auditory steady-state gamma response.
      ,
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      Aberrant developmental patterns of gamma-band response and long-range communication disruption in youths with 22q11.2 deletion syndrome.
      ), but the ability of visual cortices to generate neural oscillations has not been investigated so far.
      This study investigated oscillatory responses during visual perception and their relationship with brain development in deletion carriers to address this important question. Visual perception results from the interplay between neuronal oscillations at distinct frequency bands, with gamma- and theta-band oscillations subserving perceptual information transfer in low-level regions, while top-down beta-band oscillations convey feedback signaling according to the behavioral context (
      • Bastos A.M.
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      • Oostenveld R.
      • Dowdall J.R.
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      Visual areas exert feedforward and feedback influences through distinct frequency channels.
      ,
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      • Schoffelen J.M.
      • Kennedy H.
      • Fries P.
      Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas.
      ). Moreover, studies have shown that supragranular layers predominantly propagate feedforward information in the gamma-band frequency, while infragranular layers subserve feedback activity in the beta-band frequency (
      • Bastos A.M.
      • Vezoli J.
      • Bosman C.A.
      • Schoffelen J.M.
      • Oostenveld R.
      • Dowdall J.R.
      • et al.
      Visual areas exert feedforward and feedback influences through distinct frequency channels.
      ,
      • Michalareas G.
      • Vezoli J.
      • van Pelt S.
      • Schoffelen J.M.
      • Kennedy H.
      • Fries P.
      Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas.
      ,
      • Buffalo E.A.
      • Fries P.
      • Landman R.
      • Buschman T.J.
      • Desimone R.
      Laminar differences in gamma and alpha coherence in the ventral stream.
      ). For this reason, we additionally estimated Granger causality (GC) connectivity between high- and low-order areas in the visual system in source-reconstructed EEG data.
      We expected to find a selective deficit in stimulus-induced gamma- and theta-band responses (
      • Mukherjee A.
      • Carvalho F.
      • Eliez S.
      • Caroni P.
      • et al.
      Long-lasting rescue of network and cognitive dysfunction in a genetic schizophrenia model.
      ) and a lack of age-related increase in gamma-band power with respect to the control group as supported by studies in the homologous mouse model of 22q11DS (
      • Mukherjee A.
      • Carvalho F.
      • Eliez S.
      • Caroni P.
      • et al.
      Long-lasting rescue of network and cognitive dysfunction in a genetic schizophrenia model.
      ). Furthermore, we hypothesized that deletion carriers would express increased top-down connectivity as a compensatory mechanism for deficits in visual circuits. Finally, we conducted exploratory analyses to test the association between oscillatory response and the degree of psychotic symptoms in deletion carriers.

      Methods and Materials

      Recruitment and Assessment of Patients

      Individuals with 22q11DS and control subjects were recruited in the context of the 22q11DS Swiss Cohort (details available in the Supplement).
      The occurrence of attenuated psychotic symptoms (APSs) was assessed in deletion carriers by means of the Structured Interview for Psychosis-Risk Syndromes (
      • Miller T.J.
      • McGlashan T.H.
      • Rosen J.L.
      • Cadenhead K.
      • Cannon T.
      • Ventura J.
      • et al.
      Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: Predictive validity, interrater reliability, and training to reliability [published correction appears in Schizophr Bull 2004; 30:following 217].
      ). Deletion carriers were divided into subgroups according to the presence of moderate to severe APS symptoms, using a cut-off score of 3 or higher in at least one of the corresponding items for positive symptoms of the Structured Interview for Psychosis-Risk Syndromes.

      Participants

      Of 145 potential participants (age range = 7–30 years), 6 deletion carriers were not included in the study because of a medical history of epilepsy or epileptic seizures. Fourteen datasets (8 deletion carriers and 6 control subjects) were additionally excluded from the analyses because the number of accepted clean epochs with a correct answer was n < 40, resulting in 63 subjects with 22q11DS (mean age = 17.3 ± 6 years, 26 female) and 62 control subjects (mean age = 17.2 ± 7 years, 24 female). The participants of each group were divided into age bins: childhood (from 7 to 13 years; n = 39), adolescence (from 14 to 18 years; n = 39), and adulthood (≥19 years; n = 47) for the age-related analyses. Control subjects and deletion carriers were overall age and sex matched, as for age subgroups (Table 1).
      Table 1Demographic Information and Medical History Comprising Psychiatric Disorders According to DSM-5 and Medications Usage in Control Subjects and Deletion Carriers and in the Subgroups of Deletion Carriers Older Than 14 Years With and Without Psychotic Symptoms
      Demographic and Clinical InformationControl SubjectsDeletion Carriersp ValueNonpsychoticPsychoticp Value
      Number of Subjects (% F)62 (50%)63 (49.2%).7628 (71.4%)12 (58.3%).27
      Age, Years, Mean ± SD17.3 ± 6.117.2 ± 7.9321.6 ± 5.720.9 ± 6.1.78
      Age Range, Years7–307–30N/A14–3014–30N/A
      FSIQ, Mean ± SD110.1 ± 1775 ± 12.6<.0173.3 ± 10.570.7 ± 16.4.53
      Behavioral Performance, Number of Correct Answers, Mean ± SD158.3 ± 34.8206.4 ± 29.9<.01167.8 ± 26.7167 ± 24.9.93
      Children, n (Mean Age ± SD), % F16 (10.2 ± 2.4), 50%23 (10.4 ± 1.5), 47.8%.76N/AN/AN/A
      Adolescents, n (Mean Age ± SD), % F23 (15.7 ± 1.4), 47.8%16 (15.4 ± 1.3), 43.8%.4N/AN/AN/A
      Adults, n (Mean Age ± SD), % F23 (23.5 ± 3.3), 52.2%24 (25.4 ± 3.8), 54.2%.14N/AN/AN/A
      Subjects Medicated, n (%)024 (38.1%)N/A10 (35.7%)7 (58.3%).18
       Psychostimulants015 (23.8%)N/A7 (25%)3 (25%).95
       Antidepressants012 (19%)N/A7 (25%)5 (41.7%).29
       Antipsychotics010 (15.9%)N/A0 (0%)7 (58.3%)<.01
      Subjects Meeting Criteria for Psychiatric Diagnosis, n (%)041 (65.1%)N/A14 (50%)8 (66.7%).33
       ADHD032 (50.8%)N/A11 (39.3%)6 (50%).81
       Anxiety disorders030 (47.6%)N/A10 (35.7%)7 (58.3%).34
       Mood disorders04 (6.3%)N/A2 (7.1%)2 (16.7%).50
       Psychosis spectrum disorders013 (20.6%)N/A03 (25%)<.01
      SIPS Positive Symptoms Score, Mean ± SDN/A0.8 ± 1N/A0.3 ± 0.42.1 ± 1.2<.01
      SIPS Negative Symptoms Score, Mean ± SDN/A2.1 ± 0.9N/A2 ± 0.72.9 ± 0.9.01
      p Values refer to the comparison between groups and subgroups performed with two-tailed t test and χ2 test when appropriate.
      ADHD, attention-deficit/hyperactivity disorder; F, female; FSIQ, Full Scale IQ; N/A, not applicable; SIPS, Structured Interview for Psychosis-Risk Syndromes.

      Visual Paradigm

      The visual paradigm consisted of a centrally presented, circular sine wave grating (Figure 1). The circular grating drifted inward toward the fixation point position, and the speed of this contraction increased (velocity step at 2.2 deg/s) at a randomized time point between 750 and 3000 ms after stimulus onset (
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ,
      • Hoogenboom N.
      • Schoffelen J.M.
      • Oostenveld R.
      • Parkes L.M.
      • Fries P.
      Localizing human visual gamma-band activity in frequency, time and space.
      ). The experimental protocol comprised 240 trials divided into three runs of 80 trials. Participants were instructed to press a button as soon as they noticed a speed increase. Stimulus offset was followed by a period of 1000 ms during which subjects were given visual feedback depending on their response. Before beginning the experiment, all participants underwent a training session with one researcher to be sure that they understood the task. Behavioral measures were calculated as the percentage of correct answers of the 240 trials and average reaction time.
      Figure thumbnail gr1
      Figure 1Behavioral results. Upper panel: diagram of the inward-moving grating task. Participants are asked to report the change in speed of inward motion of the grating by button press. Lower panel: bar plots showing group and age subgroup mean and standard deviation for the percentage of correct answers and reaction times (in milliseconds). Asterisks indicate statically significant differences between groups (22q11DS < HC) and subgroups (kids < adolescents, kids < adults). 22q11DS, 22q11.2 deletion syndrome; HC, healthy control.

      EEG Data Acquisition During Visual Paradigm and Preprocessing

      EEG data were continuously recorded with a sampling rate of 1000 Hz using a 256-electrode Hydrocel cap (Magstim-EGI) referenced to the vertex (Cz). The impedance was kept below 30 kΩ for all electrodes and below 10 kΩ for the reference and ground electrodes.
      The preprocessing steps, including bandpass filtering, exclusion of artifactual periods, interpolation of noisy channels, and re-referencing (
      • Makeig S.
      • Jung T.P.
      • Bell A.J.
      • Ghahremani D.
      • Sejnowski T.J.
      Blind separation of auditory event-related brain responses into independent components.
      ,
      • Jung T.P.
      • Makeig S.
      • Westerfield M.
      • Townsend J.
      • Courchesne E.
      • Sejnowski T.J.
      Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects.
      ,
      • Perrin F.
      • Pernier J.
      • Bertrand O.
      • Echallier J.F.
      Spherical splines for scalp potential and current density mapping.
      ) were performed using the free academic software Cartool (
      • Mancini V.
      • Rochas V.
      • Seeber M.
      • Roehri N.
      • Rihs T.A.
      • Ferat V.
      • et al.
      Aberrant developmental patterns of gamma-band response and long-range communication disruption in youths with 22q11.2 deletion syndrome.
      ,
      • Cantonas L.M.
      • Mancini V.
      • Rihs T.A.
      • Rochas V.
      • Schneider M.
      • Eliez S.
      • Michel C.M.
      Abnormal auditory processing and underlying structural changes in 22q11.2 deletion syndrome.
      ). For further details, see the Supplement.

      EEG Time-Frequency and Intertrial Phase Coherence Analyses

      Only epochs with correct behavioral responses were considered for EEG analysis. Owing to the imbalance between the number of correct responses between groups, a percentage of the total epochs based on the distribution of the entire sample was randomly selected in control subjects to have a comparable number of epochs (control subjects: 129.2 ± 33.4; 22q11DS: 120.9 ± 49.5).
      Time-frequency analysis was performed using Morlet transform (frequencies from 2 to 120 Hz, centered on steps of 2 Hz, with adapted resolution according to the full width at half maximum scheme) in MATLAB (version 2018b; The MathWorks, Inc.). Time epochs from −1.5 to +1.5 seconds relative to the stimulus onset were averaged to event-related spectral perturbations (ERSPs) and normalized by the baseline period (−1.5 to −0.3 seconds) (
      • Neuper C.
      • Pfurtscheller G.
      Event-related dynamics of cortical rhythms: Frequency-specific features and functional correlates.
      ). Intertrial phase coherence (ITPC) amplitudes were also calculated from Morlet transform (
      • Delorme A.
      • Makeig S.
      EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
      ). At the sensor level, a cluster of predefined occipitoparietal electrodes was considered for further analyses. To investigate the interaction of spectral response with behavioral and clinical variables, neurophysiological indices were also calculated from averages of the ERSP along frequency bands of interest for theta, alpha/beta, and gamma and in time from 0.25 to 0.75 seconds for gamma and alpha/beta and from 0 to 0.4 seconds for theta.
      For source analysis, the inverse solution (IS) was computed using Cartool version 61 based on individual T1-weighted images preprocessed in FreeSurfer image analysis suite, version 6.0 (
      • Fischl B.
      ). An approximate number of 5000 solution points were distributed in the individually segmented gray matter mask. We used the Locally Spherical Model with Anatomical Constraints method for the lead field computation, which was age adjusted to reflect differences across age in skull conductivity and thickness (
      • Michel C.M.
      • Brunet D.
      EEG source imaging: A practical review of the analysis steps.
      ,
      • Brunet D.
      • Murray M.M.
      • Michel C.M.
      Spatiotemporal analysis of multichannel EEG: CARTOOL.
      ). A distributed linear IS (Local AutoRegressive Average) was used to compute a transformation matrix from sensor level to IS (
      • Michel C.M.
      • Brunet D.
      EEG source imaging: A practical review of the analysis steps.
      ). We obtained an individual Desikan-Killiany parcellation (
      • Desikan R.S.
      • Ségonne F.
      • Fischl B.
      • Quinn B.T.
      • Dickerson B.C.
      • Blacker D.
      • et al.
      An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
      ) from FreeSurfer. This individual parcellation natively aligned on the brain of each individual was then used to label the 5000 solution points from the IS model in 84 regions of interest (ROIs) covering cortical and subcortical structures. Using this individual IS model, time-frequency decomposition data from the surface were projected to the source space level and gathered in ROIs representing the whole brain.

      GC Analysis

      GC functional connectivity was computed in source space with a nonparametric approach (
      • Dhamala M.
      • Rangarajan G.
      • Ding M.
      Analyzing information flow in brain networks with nonparametric Granger causality.
      ) implemented in the MATLAB Toolbox FieldTrip (
      • Oostenveld R.
      • Fries P.
      • Maris E.
      • Schoffelen J.M.
      FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.
      ). First, preprocessed EEG data were transformed to the singular value decomposition of the signal for each ROI using the individual IS model matrix and Desikan-Killiany parcellation (
      • Mancini V.
      • Rochas V.
      • Seeber M.
      • Roehri N.
      • Rihs T.A.
      • Ferat V.
      • et al.
      Aberrant developmental patterns of gamma-band response and long-range communication disruption in youths with 22q11.2 deletion syndrome.
      ). To increase trials number, we split epochs into 2 × 0.25-second segments (
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ,
      • Michalareas G.
      • Vezoli J.
      • van Pelt S.
      • Schoffelen J.M.
      • Kennedy H.
      • Fries P.
      Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas.
      ) of the first 0.5 second after 0.25 second from the stimulus onset (from 0.25 to 0.75 seconds). Nonoverlapping ROIs activated by the task belonging to high-order (superior frontal gyrus [SFG]) and low-order areas (cuneus, lateral occipital cortex [LOC]) in the visual system were selected, and GC data from each bilateral pair were averaged over hemisphere. Spectral density matrices were estimated from Fast Fourier–transformed data (0.25–0.75 seconds, 1–80 Hz) with 5-Hz frequency smoothing, matrix factorization, and variance decomposition. Finally, the directionality of connectivity (i.e., feedback vs. feedforward activity) was estimated by computing the direct asymmetry index (
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ,
      • Michalareas G.
      • Vezoli J.
      • van Pelt S.
      • Schoffelen J.M.
      • Kennedy H.
      • Fries P.
      Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas.
      ).

      Statistics

      Statistical analyses were performed with MATLAB version 2018a. Independent two-tailed t tests (α level = 0.05) were performed to compare ITPC (from −0.5 to 0.5 seconds), ERSPs (from −0.5 to 0.75 seconds), and GC estimates between control subjects and individuals with 22q11DS.
      The age-by-group interaction in behavioral and neurophysiological data was analyzed with two-way analyses of variance with the hierarchical between-subject factors group (control subjects, patients) and age (kids, adolescents, adults), and post hoc analyses were corrected for multiple comparisons using Tukey tests. Multiple linear regression was used to investigate correlations between clinical variables [including behavioral performance, clinical measures, and Full Scale IQ (
      • Weschler D.
      WAIS-IV: Administration and Scoring Manual.
      )] and neurophysiological data extracted from time-frequency decomposition in deletion carriers. False discovery rate (FDR) correction for multiple comparisons with the Benjamini-Hochberg method (
      • Benjamini Y.
      • Hochberg Y.
      Controlling the false discovery rate: A practical and powerful approach to multiple testing.
      ) was applied to t tests, correcting for the number of frequency bins and time points at sensor level and, for the number of frequency bins, time points and ROIs at the source level.
      FDR correction was also applied for correction of GC between-groups comparison, correcting for the number of frequency bins and couples of nodes tested. FDR-corrected values are reported for the statistically significant time points, indicating the time window and frequency band of significance. Effect sizes were estimated with Cohen’s d.

      Results

      Behavioral Data Analysis

      A two-way analysis of variance was conducted to examine the effects of group and developmental stage on the percentage of correct responses (Figure 1). There was a significant difference between deletion carriers and healthy control subjects (66% vs. 86%; F1,119 = 73.9, p < .001, partial η2 = 0.38) and across age bins (F2,119 = 26.3, p < .001, partial η2 = 0.30). However, no age-by-group interaction was detected. Post hoc Tukey tests showed that the performance of children was significantly reduced compared with both adolescents and adults (p < .001). No differences were found for average reaction times (457.3 ± 91.2 vs. 460.2 ± 234; F1,119 = 0.02, p = .98, partial η2 = 0.001).

      ERSP Differences Between Control Subjects and Deletion Carriers

      Statistically significant differences between control subjects and deletion carriers were found at gamma- and theta-band frequencies over parieto-occipital electrodes, with deletion carriers having a decreased high and low gamma response during a sustained period (58–68 Hz, 0.25–0.75 seconds, t123 = 3.5, p < .001, d = 0.98; 28–44 Hz, t123 = 3, p = .0135, d = 0.72) and a decreased early theta response (4–10 Hz, 0–0.4 seconds, t123 = 3.4, p < .001, d = 0.78) (Figure 2A). In addition, differences in ITPC were identified in the alpha frequency range (8–14 Hz, 0–0.5 seconds, t123 = 3.8, p = .04) and earlier in beta/low gamma range (26–36 Hz, 0–0.2 seconds, t123 = 3.5, p = .006) (Figure 2B).
      Figure thumbnail gr2
      Figure 2Time-frequency and intertrial phase coherence comparison between control subjects and deletion carriers. (A) Time-frequency plots displaying the average pre- and poststimulus event-related spectral perturbation in control subjects and deletion carriers over a cluster of parieto-occipital electrodes. The outlined dotted boxes highlight the time window of statistically significant group differences in high gamma (58–68 Hz), low gamma (28–44 Hz), and theta power (4–10 Hz). On the right side is delta event-related spectral perturbation, showing T values for theta band and high and low gamma band for the cluster of predetermined electrodes. (B) Time-frequency plots displaying the average pre- and poststimulus intertrial phase coherence in control subjects and deletion carriers over a cluster of parieto-occipital electrodes. The outlined dotted boxes highlight the time window of statistically significant group differences in alpha (8–14 Hz) and low gamma (26–36 Hz) bands. On the right side is delta intertrial phase coherence showing T values in alpha and low gamma bands for the cluster of predetermined electrodes. Power values are expressed in %. 22q11DS, 22q11.2 deletion syndrome.
      Source space analysis revealed a decreased high gamma-band response (58–68 Hz) in deletion carriers in the bilateral cuneus and precuneus and right pericalcarine and superior parietal cortices (Figure 3A, C). In contrast, lower theta (4–8 Hz) responses in deletion carriers were localized to a wider occipital-temporo-parietal network, comprising the bilateral cuneus, pericalcarine cortex, LOC, lingual gyrus, precuneus, superior and inferior parietal cortices, and inferior temporal cortex (Figure 2B).
      Figure thumbnail gr3
      Figure 3Source space time-frequency analysis between control subjects and deletion carriers. (A) Brain map with regions showing a statistically significant decreased gamma response (58–68 Hz, 0–0.75 seconds) in deletion carriers. The colormap represents T values for differences between control subjects and deletion carriers plotted on the brain. (B) Brain map with regions showing a statistically significant lower theta response (4–10 Hz, 0–0.5 seconds) in deletion carriers during the first 0.5 second of the response. (C) Time-frequency plots for each brain region with decreased gamma response in deletion carriers. The colormap represents power values expressed in %. Regions of interest: a = superior parietal cortex; b = inferior parietal cortex; c = lateral occipital cortex; d = precuneus; e = cuneus; f = lingual gyrus; g = pericalcarine cortex; h = inferior temporal cortex. 22q11DS, 22q11.2 deletion syndrome; HC, healthy control.

      Between-Groups Differences in Age-Related Gamma, Alpha/Beta, and Theta Responses

      To test the age-by-group interaction, we conducted a two-way analysis of variance for each frequency band of interest (i.e., theta, alpha/beta, and gamma) (
      • Bastos A.M.
      • Vezoli J.
      • Bosman C.A.
      • Schoffelen J.M.
      • Oostenveld R.
      • Dowdall J.R.
      • et al.
      Visual areas exert feedforward and feedback influences through distinct frequency channels.
      ,
      • Michalareas G.
      • Vezoli J.
      • van Pelt S.
      • Schoffelen J.M.
      • Kennedy H.
      • Fries P.
      Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas.
      ). We found a statistically significant effect of group (F1,119 = 21.5, p < .001, partial η2 = 0.16) and age (F2,119 = 5.3, p = .007, partial η2 = 0.09) on gamma-band responses (58–68 Hz, 0.25–0.75 s), with an age-by-group interaction (F2,119 = 3.2, p = .04, partial η2 = 0.6). Post hoc analyses with Tukey test(s) showed that gamma-band response in control subjects was significantly higher in adults than in children (p = .005) and adolescents (p = .02).
      There was also a significant effect of group (F1,119 = 5.2, p = .025, partial η2 = 0.06) and age (F2,119 = 8.6, p < .001, partial η2 = 0.15) on alpha/beta-band desynchronization (10–25 Hz, 0.25–0.75 seconds), without an age-by-group interaction. Post hoc analyses showed that alpha/beta desynchronization in both groups was significantly higher in adults than in children (p < .001) and adolescents (p = .012) in both groups. Finally, we found a statistically significant effect of group (F1,119 = 22.8, p < .001, partial η2 = 0.19) and age (F2,119 = 8.9, p < .001, partial η2 = 0.16) on theta-band responses (4–8 Hz, 0–0.5 seconds), with an age-by-group interaction (F2,119 = 3.2, p = .04, partial η2 = 0.09). Post hoc analyses showed that in addition to the higher theta-band response in control subjects compared with deletion carriers, the theta-band response in control subjects was significantly higher in children than in adults (p = .003) and adolescents (p = .011) (Figure 4). To verify whether the lack of statistically significant interaction with age for gamma-band response (58–68 Hz, 0.25–0.75 seconds) in deletion carriers depended on a relatively low sample size, we performed power analyses. With α = 0.05 and power = 0.80, the projected sample size needed is approximately n = 388 for the comparison between adults and adolescents and n = 1000 for the comparison between adults and children. Given the magnitude of the projected sample size to find differences between age subgroups, we concluded that the age-by-group interaction observed reflected blunted developmental trajectories in deletion carriers.
      Figure thumbnail gr4
      Figure 4Developmental patterns of oscillatory response. Upper panel: time-frequency plots are shown for each age bin (childhood, adolescence, and adulthood) in the two groups compared: control subjects (above the arrow) and deletion carriers (below the arrow). Statistically significant differences were found only between adult subgroups and between the adult control group vs. the children and adolescent control groups. Lower panel: age subgroups comparison between control subjects and deletion carriers for averaged theta (4–10 Hz), alpha/beta (10–25 Hz), and gamma power (58–68 Hz) over a parieto-occipital cluster of electrodes. Power values are expressed in %. 22q11DS, 22q11.2 deletion syndrome.

      Correlation With Behavioral Performance and Full Scale IQ

      We fitted a regression model to test the association between behavioral performance and averaged oscillatory response in high gamma (58–68 Hz), low gamma (28–44 Hz), theta (4–8 Hz), and alpha/beta (10–25 Hz) bands in deletion carriers and control subjects. While the overall regression was not statistically significant for either of the groups, we found that alpha/beta-band response (0.25–0.75 seconds) significantly predicted the number of correct responses (β = −0.54, p = .028) in deletion carriers. In addition, another regression model was fitted to test the association between Full Scale IQ and the neurophysiological data described above, but the overall regression was not statistically significant, and there was no significant interaction with any variable in any group.

      GC Connectivity

      We found decreased top-down connectivity from the SFG to the LOC at beta frequency (22–40 Hz, t123 = −3.18, p = .004, d = −0.7) in control subjects (Figure 5). In addition, control subjects also had increased bottom-up connectivity from the cuneus to the LOC (65–75 Hz, t123 = 3.38, p = .004, d = 0.65) and decreased LOC to cuneus connectivity (23–40 Hz, t123 = −3.35, p = .015, d = −0.8) as compared with deletion carriers. The directed asymmetry indices were negative for SFG to LOC and for LOC to cuneus connectivity and positive for cuneus to LOC connectivity, indicating feedback and feedforward flow of information between the nodes, respectively. No between-groups differences were found for SFG to cuneus GC connectivity.
      Figure thumbnail gr5
      Figure 5Between-groups GC connectivity differences. Results of the comparison between deletion carriers and control subjects of GC connectivity estimates computed between 0.25 and 0.75 seconds after stimulus. GC values for each group are plotted across the frequency spectrum with error bars indicating SEM, and an arrow indicating the frequency range of significant group effects. The directed asymmetry indices were negative for SFG to LOC and for LOC to Cun connectivity and positive for Cun to LOC connectivity, indicating feedback and feedforward flow of information between the nodes, respectively. On the bottom of the figure, increased (red) and decreased (light blue) GC connections in deletion carriers are plotted on the surface of a standard Montreal Neurological Institute brain in sagittal and coronal planes. 22q11DS, 22q11.2 deletion syndrome; Cun, cuneus; GC, Granger causality; HC, healthy control; LOC, lateral occipital cortex; SFG, superior frontal gyrus.

      Psychotic Symptoms and Brain Oscillations

      Deletion carriers with APSs (n = 12) were compared with a group of age-matched nonpsychotic individuals with 22q11DS (n = 28). At sensor level, there was a significant reduction in high gamma-band responses (58–68 Hz) in deletion carriers with APS as compared with nonpsychotic deletion carriers, which, however, did not survive FDR correction (Figure S1). We performed a power analysis based on these results and with α = 0.05 and power = 0.80, the projected sample size needed to find a difference in gamma-band response (58–68 Hz, 0.25–0.75 seconds) between the two groups is approximately n = 64.
      A regression model was fitted to test the association between Structured Interview for Psychosis-Risk Syndromes positive and negative subscales and averaged gamma-, theta-, or beta-band ERSPs or averaged alpha/beta ITPC amplitude in deletion carriers. The overall regression was not statistically significant, and there was no significant interaction with any variable.

      Discussion

      In this study, we showed decreased theta- and gamma-band responses to visual stimuli in deletion carriers, together with an increase in top-down connectivity mediated by frontal cortices. In addition, while the maturational patterns of gamma- and theta-band responses were disrupted in individuals with 22q11DS, the development of alpha/beta responses was preserved. Together, these findings provide novel evidence for the involvement of neural oscillations in visual circuit dysfunctions in 22q11DS.

      Impaired Theta- and Gamma-Band Responses to Visual Stimuli and Behavioral Correlates

      The main finding was a marked decrease in the stimulus-induced power of low/high gamma- and theta-band responses in deletion carriers while alpha/beta desynchronization was intact. Source analysis localized group differences to occipital-parietal regions. The recruitment of these regions is consistent with previous studies (
      • Shaw A.D.
      • Knight L.
      • Freeman T.C.A.
      • Williams G.M.
      • Moran R.J.
      • Friston K.J.
      • et al.
      Oscillatory, computational, and behavioral evidence for impaired GABAergic inhibition in schizophrenia.
      ,
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ,
      • Michalareas G.
      • Vezoli J.
      • van Pelt S.
      • Schoffelen J.M.
      • Kennedy H.
      • Fries P.
      Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas.
      ). In contrast, decreased theta/gamma-band activity in visual areas in deletion carriers highlights the involvement of aberrant circuity in sensory areas in 22q11DS. Compromised local circuit activity in V1 with decreased stimulus-elicited gamma- and theta-band responses has been similarly identified in the homologous mice model of 22q11DS (
      • Hamm J.P.
      • Peterka D.S.
      • Gogos J.A.
      • Yuste R.
      Altered cortical ensembles in mouse models of schizophrenia.
      ). Several genes within the 22q11.2 region are implicated in interneuron migration (
      • Meechan D.W.
      • Tucker E.S.
      • Maynard T.M.
      • LaMantia A.S.
      Cxcr4 regulation of interneuron migration is disrupted in 22q11.2 deletion syndrome.
      ,
      • Toritsuka M.
      • Kimoto S.
      • Muraki K.
      • Landek-Salgado M.A.
      • Yoshida A.
      • Yamamoto N.
      • et al.
      Deficits in microRNA-mediated Cxcr4/Cxcl12 signaling in neurodevelopmental deficits in a 22q11 deletion syndrome mouse model.
      ) and GABAergic and glutamatergic signaling (
      • Motahari Z.
      • Moody S.A.
      • Maynard T.M.
      • Lamantia A.S.
      In the line-up: Deleted genes associated with DiGeorge/22q11.2 deletion syndrome: Are they all suspects?.
      ). Given the involvement of GABAergic and glutamatergic neural transmission in the generation of gamma-band oscillations (
      • Cardin J.A.
      • Carlén M.
      • Meletis K.
      • Knoblich U.
      • Zhang F.
      • Deisseroth K.
      • et al.
      Driving fast-spiking cells induces gamma rhythm and controls sensory responses.
      ), it is possible that the gamma-band response impairment identified in mice and human deletion carriers may be associated with the haploinsufficiency of key genes.
      In contrast to the impairment in gamma-band responses, alpha/beta desynchronization was spared in individuals with 22q11DS. Furthermore, the subgroup of adult deletion carriers displayed even enhanced desynchronization compared with control subjects, which correlated with performance levels. Gamma and alpha/beta oscillations have been proposed to subserve distinct roles in information processing as well as involve different neural substrates. While gamma oscillations reflect the feedforward propagation of sensory stimuli (
      • Hoogenboom N.
      • Schoffelen J.M.
      • Oostenveld R.
      • Parkes L.M.
      • Fries P.
      Localizing human visual gamma-band activity in frequency, time and space.
      ,
      • Fries P.
      Rhythms for cognition: Communication through coherence.
      ), alpha and beta oscillations mediate top-down information representing the attention allocation toward visual stimuli (
      • Klimesch W.
      α-band oscillations, attention, and controlled access to stored information.
      ,
      • Rihs T.A.
      • Michel C.M.
      • Thut G.
      A bias for posterior α-band power suppression versus enhancement during shifting versus maintenance of spatial attention.
      ,
      • Romei V.
      • Rihs T.
      • Brodbeck V.
      • Thut G.
      Resting electroencephalogram alpha-power over posterior sites indexes baseline visual cortex excitability.
      ). Moreover, the generation of distinct rhythms is also associated with different cortical layers (
      • Fries P.
      Rhythms for cognition: Communication through coherence.
      ,
      • Shaw A.D.
      • Moran R.J.
      • Muthukumaraswamy S.D.
      • Brealy J.
      • Linden D.E.
      • Friston K.J.
      • Singh K.D.
      Neurophysiologically-informed markers of individual variability and pharmacological manipulation of human cortical gamma.
      ). Gamma oscillations are assumed to arise from supragranular layers, while alpha/beta oscillations arise from infragranular layers. Studies in a mouse model of 22q11DS highlighted a disruption in the proliferation of basal progenitors, which predominantly give rise to supragranular pyramidal cells later in life (
      • Meechan D.W.
      • Tucker E.S.
      • Maynard T.M.
      • LaMantia A.S.
      Diminished dosage of 22q11 genes disrupts neurogenesis and cortical development in a mouse model of 22q11 deletion/DiGeorge syndrome.
      ), and altered migration of interneurons (
      • Meechan D.W.
      • Tucker E.S.
      • Maynard T.M.
      • LaMantia A.S.
      Cxcr4 regulation of interneuron migration is disrupted in 22q11.2 deletion syndrome.
      ,
      • Toritsuka M.
      • Kimoto S.
      • Muraki K.
      • Landek-Salgado M.A.
      • Yoshida A.
      • Yamamoto N.
      • et al.
      Deficits in microRNA-mediated Cxcr4/Cxcl12 signaling in neurodevelopmental deficits in a 22q11 deletion syndrome mouse model.
      ). Thus, the dissociation between impaired theta/gamma and preserved alpha/beta-band responses identified in our data may reflect a selective impairment of supragranular projection neurons and interneuron dysfunction in individuals with 22q11DS. Future research is needed to test this hypothesis.

      Increased Alpha/Beta Desynchronization and Top-Down Connectivity in Deletion Carriers

      We further explored frequency-resolved directed connectivity between high- and low-order visual areas and observed enhanced feedback information flow from the prefrontal cortex to the LOC at beta frequencies, while the feedforward communication in higher frequencies from V1 to LOC was impaired in deletion carriers. In normal conditions, heightened top-down control exerted over visual areas leads to increased gamma-band power (
      • Grothe I.
      • Neitzel S.D.
      • Mandon S.
      • Kreiter A.K.
      Switching neuronal inputs by differential modulations of gamma-band phase-coherence.
      ,
      • Lee J.H.
      • Whittington M.A.
      • Kopell N.J.
      Top-down beta rhythms support selective attention via interlaminar interaction: A model.
      ), thus modulating sensory processing according to the behavioral context (
      • Gilbert C.D.
      • Li W.
      Top-down influences on visual processing.
      ). However, despite increased top-down modulation of lower-order areas, deletion carriers display profound impairment in gamma-band response in the primary visual cortex and decreased bottom-up gamma signaling between primary and secondary visual areas.
      Increased top-down and decreased bottom-up connectivity has been also identified in patients at clinical high risk for psychosis and patients with first episode of psychosis (
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ), suggesting a close overlap between circuit deficits caused by 22q11.2 deletion and early-stage psychosis. Moreover, previous ERSP studies in 22q11DS found enhanced feedback activity (
      • Magnée M.J.C.M.
      • Lamme V.A.F.
      • de Sain-van der Velden M.G.M.
      • Vorstman J.A.S.
      • Kemner C.
      Proline and COMT Status affect visual connectivity in children with 22q11.2 deletion syndrome.
      ) and increased amplitude in negative late-latency components localized to the frontal cortex (
      • Biria M.
      • Tomescu M.I.
      • Custo A.
      • Cantonas L.M.
      • Song K.W.
      • Schneider M.
      • et al.
      Visual processing deficits in 22q11.2 deletion syndrome.
      ). Overall, elevated top-down modulation of visual areas in this study may constitute a compensatory mechanism for impaired feedforward activity in early sensory regions.

      Differential Impact of Age on Frequency Bands

      Our final aim was to investigate how neural oscillations during visual perception change during brain development. In control subjects, we identified age-related changes in induced power for theta-band (4–8 Hz), alpha/beta-band (10–25 Hz), and high gamma-band (58–68 Hz) oscillations during adolescence, which are consistent with previous findings (
      • Uhlhaas P.J.
      • Roux F.
      • Singer W.
      • Haenschel C.
      • Sireteanu R.
      • Rodriguez E.
      The development of neural synchrony reflects late maturation and restructuring of functional networks in humans.
      ). Remarkably, while deletion carriers exhibited preserved developmental patterns for alpha/beta frequencies, the age-related increase in gamma-band responses was largely absent.
      Adolescence is characterized by the protracted maturation of both GABAergic neural transmission (
      • Hashimoto T.
      • Nguyen Q.L.
      • Rotaru D.
      • Keenan T.
      • Arion D.
      • Beneyto M.
      • et al.
      Protracted developmental trajectories of GABAA receptor α1 and α2 subunit expression in primate prefrontal cortex.
      ), including parvalbumin interneurons (
      • Morishita H.
      • Kundakovic M.
      • Bicks L.
      • Mitchell A.
      • Akbarian S.
      Interneuron epigenomes during the critical period of cortical plasticity: Implications for schizophrenia.
      ), and NMDA receptor expression (
      • Wang H.X.
      • Gao W.J.
      Cell type-specific development of NMDA receptors in the interneurons of rat prefrontal cortex.
      ) that could underlie the late development of high-frequency oscillations (
      • Uhlhaas P.J.
      • Singer W.
      The development of neural synchrony and large-scale cortical networks during adolescence: Relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis.
      ,
      • Uhlhaas P.J.
      • Roux F.
      • Rodriguez E.
      • Rotarska-Jagiela A.
      • Singer W.
      Neural synchrony and the development of cortical networks.
      ). Accordingly, it is conceivable that the failure to express adult-level gamma-band responses in deletion carriers is related to aberrant maturation of GABAergic and glutamatergic circuit motifs that could potentially also contribute to the risk of developing psychosis in 22q11 deletion carriers.
      These findings are in line with previous studies showing reduced gamma-band response to auditory stimuli in deletion carriers and a similar developmental profile (
      • Mancini V.
      • Rochas V.
      • Seeber M.
      • Roehri N.
      • Rihs T.A.
      • Ferat V.
      • et al.
      Aberrant developmental patterns of gamma-band response and long-range communication disruption in youths with 22q11.2 deletion syndrome.
      ). In both deletion carriers and patients with idiopathic psychotic disorders, decreased gamma-band responses to auditory stimuli have been identified predominantly in the temporal cortex (
      • Reilly T.J.
      • Nottage J.F.
      • Studerus E.
      • Rutigliano G.
      • De Micheli A.I.
      • Fusar-Poli P.
      • McGuire P.
      Gamma band oscillations in the early phase of psychosis: A systematic review.
      ,
      • Larsen K.M.
      • Pellegrino G.
      • Birknow M.R.
      • Kjær T.N.
      • Baaré W.F.C.
      • Didriksen M.
      • et al.
      22q11.2 deletion syndrome is associated with impaired auditory steady-state gamma response.
      ,
      • Mancini V.
      • Rochas V.
      • Seeber M.
      • Roehri N.
      • Rihs T.A.
      • Ferat V.
      • et al.
      Aberrant developmental patterns of gamma-band response and long-range communication disruption in youths with 22q11.2 deletion syndrome.
      ,
      • Thuné H.
      • Recasens M.
      • Uhlhaas P.J.
      The 40-Hz auditory steady-state response in patients with schizophrenia: A meta-analysis.
      ). Likewise, gamma oscillation impairment during visual processing has been mapped to the occipital cortex (
      • Shaw A.D.
      • Knight L.
      • Freeman T.C.A.
      • Williams G.M.
      • Moran R.J.
      • Friston K.J.
      • et al.
      Oscillatory, computational, and behavioral evidence for impaired GABAergic inhibition in schizophrenia.
      ,
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ). Studies using magnetic resonance spectroscopy (MRS) and positron emission tomography imaging have demonstrated a correlation between gamma-band power during auditory and visual tasks and GABA (gamma-aminobutyric acid) concentration or GABAA receptor density, respectively (
      • Kujala J.
      • Jung J.
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      ,
      • Balz J.
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      • Mekle R.
      • Schubert F.
      • Aydin S.
      • et al.
      GABA concentration in superior temporal sulcus predicts gamma power and perception in the sound-induced flash illusion.
      ). Thus, findings of gamma-band impairment are in agreement with postmortem and MRS studies in patients with schizophrenia showing a marked reduction of GABA concentration in occipital and auditory cortices (
      • Yoon J.H.
      • Maddock R.J.
      • Rokem A.
      • Silver M.A.
      • Minzenberg M.J.
      • Ragland J.D.
      • Carter C.S.
      GABA concentration is reduced in visual cortex in schizophrenia and correlates with orientation-specific surround suppression.
      ,
      • McCutcheon R.A.
      • Krystal J.H.
      • Howes O.D.
      Dopamine and glutamate in schizophrenia: Biology, symptoms and treatment.
      ,
      • de Jonge J.C.
      • Vinkers C.H.
      • Hulshoff Pol H.E.
      • Marsman A.
      GABAergic mechanisms in schizophrenia: Linking postmortem and in vivo studies.
      ,
      • Thakkar K.N.
      • Rösler L.
      • Wijnen J.P.
      • Boer V.O.
      • Klomp D.W.J.
      • Cahn W.
      • et al.
      7T proton magnetic resonance spectroscopy of gamma-aminobutyric acid, glutamate, and glutamine reveals altered concentrations in patients with schizophrenia and healthy siblings.
      ).
      An interesting perspective is that the identified deficits in gamma-band response to sensory stimuli may be related to the disruption of GABAergic signaling also in 22q11DS. Studies conducted so far in 22q11DS to examine GABA are conflicting, with a lack of human MRS evidence for altered GABA concentration in the anterior cingulate cortex (
      • Vingerhoets C.
      • Tse D.H.
      • van Oudenaren M.
      • Hernaus D.
      • van Duin E.
      • Zinkstok J.
      • et al.
      Glutamatergic and GABAergic reactivity and cognition in 22q11.2 deletion syndrome and healthy volunteers: A randomized double-blind 7-Tesla pharmacological MRS study.
      ) but findings of abnormal GABA release and response to GABAA receptor antagonists in mice models (
      • Kimoto S.
      • Muraki K.
      • Toritsuka M.
      • Mugikura S.
      • Kajiwara K.
      • Kishimoto T.
      • et al.
      Selective overexpression of Comt in prefrontal cortex rescues schizophrenia-like phenotypes in a mouse model of 22q11 deletion syndrome.
      ). Such discrepancies could be explained by inherent limitations of the MRS technique to distinguish between intra- and extracellular compartments (
      • Myers J.F.M.
      • Evans C.J.
      • Kalk N.J.
      • Edden R.A.E.
      • Lingford-Hughes A.R.
      Measurement of GABA using J-difference edited 1H-MRS following modulation of synaptic GABA concentration with tiagabine.
      ) and the choice of the explored region. Future studies are required to assess GABA concentration in brain regions implicated in sensory processing and to link it to gamma-band response in 22q11DS.

      Limitations

      First, these data are based on cross-sectional findings. Second, despite previous research showing an increasing reduction of gamma-band response throughout the progression of psychosis (
      • Grent-’t-Jong T.
      • Gajwani R.
      • Gross J.
      • Gumley A.I.
      • Krishnadas R.
      • Lawrie S.M.
      • et al.
      Association of magnetoencephalographically measured high-frequency oscillations in visual cortex with circuit dysfunctions in local and large-scale networks during emerging psychosis.
      ), no statistically significant differences in ERSP or ITPC were found between deletion carriers with and without APSs. Our exploratory analysis highlighted that the sample size for this subanalysis was slightly underpowered. Thus, we can hypothesize that given the relevance of deficits of visuospatial perception in all the subjects with a 22q11.2 microdeletion, a further decline in gamma-band response to visual stimuli in subjects endorsing psychotic symptoms may be harder to capture with relatively small sample sizes. Future studies with an adequate sample size are required to further explore differences in gamma-band response to visual stimuli between deletion carriers with and without APSs.

      Conclusions

      This study offers novel insight into the neurobiology of visual circuit deficits in individuals with 22q11DS. Specifically, our findings suggest that impairments in gamma-band responses may lead to decreased bottom-up signaling, which in turn is associated with enhanced recruitment of top-down attentional control. Our data, by highlighting the importance of early intervention to improve developmental trajectories during critical phases of brain development, could potentially inform novel treatment strategies that target circuit deficits underlying visual impairments and the associated neurobiological mechanisms in deletion carriers.

      Acknowledgments and Disclosures

      This work was supported by research grants from the Swiss National Science Foundation (Grant Nos. 324730_144260 and 320030-179404 [to SE] and Grant No. 320030_184677 [to CMM]) and a National Centre of Competence in Research Synapsy grant (Grant No. 51NF40-185897 [to SE and CMM]). This study was also supported by the Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland.
      We thank all the families who contributed to the study as well as the family associations (Generation 22, Connect 22, and Relais 22) for their ongoing support. We particularly thank the managers and operators of the EEG and MRI platforms Gwenaël Birot, Roberto Martuzzi and Loan Mattera. Special thanks go to Virginie Pouillard, Eva Micol and Tereza Kotalova for coordinating the project, to Lucia Cantonas, Johanna Maeder, Joëlle Bagautdinova, Lydia Dubourg, Farnaz Delavari and Karin Bortolin for their help in the acquisition of the data and to Hanna Thuné for her help with the EEG paradigm implementation.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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      Linked Article

      • 22q11.2 Deletion Syndrome as a Neural Model for Schizophrenia
        Biological PsychiatryVol. 92Issue 5
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          Schizophrenia is a severe condition characterized by differences in perception, thought, emotions, and behavior. It is an incapacitating disease, with tremendous human cost: those living with this condition face a double illness—schizophrenia itself and the associated stigma and discrimination, which tragically impact people’s lives and clinical outcomes.
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