CoChat
Sign Up
Cited by 14Genomic variations and chromosomal abnormalitiesFunctional Brain Connectivity StudiesGenetics and Neurodevelopmental Disorders

Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk

Open access
Dima, Diana C., Adams, Rachael, Linden, Stefanie C., Baird, Alister, Smith, Jacqueline, Foley, Sonya, Perry, Gavin, Routley, Bethany C. +9 more·2020·Translational Psychiatry
DOI 10.1038/s41398-020-00998-wPMC PMC7506525

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Rare copy number variants associated with increased risk for neurodevelopmental and psychiatric disorders (referred to as ND-CNVs) are characterized by heterogeneous phenotypes thought to share a considerable degree of overlap. Altered neural integration has often been linked to psychopathology and is a candidate marker for potential convergent mechanisms through which ND-CNVs modify risk; however, the rarity of ND-CNVs means that few studies have assessed their neural correlates. Here, we used magnetoencephalography (MEG) to investigate resting-state oscillatory connectivity in a cohort of 42 adults with ND-CNVs, including deletions or duplications at 22q11.2, 15q11.2, 15q13.3, 16p11.2, 17q12, 1q21.1, 3q29, and 2p16.3, and 42 controls. We observed decreased connectivity between occipital, temporal, and parietal areas in participants with ND-CNVs. This pattern was common across genotypes and not exclusively characteristic of 22q11.2 deletions, which were present in a third of our cohort. Furthermore, a data-driven graph theory framework enabled us to successfully distinguish participants with ND-CNVs from unaffected controls using differences in node centrality and network segregation. Together, our results point to alterations in electrophysiological connectivity as a putative common mechanism through which genetic factors confer increased risk for neurodevelopmental and psychiatric disorders.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41398-020-00998-w

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Aberrant brain connectivity has been observed using resting-state MEG in adults with a wide range of neurodevelopmental CNVs including 15q11.2, although primary abnormalities involved decreased connectivity between occipital, temporal, and parietal areas and were only measured in 7 15q11.2 deletion carriers.

    “Aberrant brain connectivity has been observed using resting-state MEG in adults with a wide range of neurodevelopmental CNVs including 15q11.2 [74], although primary abnormalities involved decreased connectivity between occipital, temporal, and parietal areas and were only measured in 7 15q11.2 deletion carriers.”

    From Linking haploinsufficiency of the autism- and schizophrenia-associated gene Cyfip1 with striatal-limbic-cortical network dysfunction and cognitive inflexibility (2024) · cites this paper as [74]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 60

  1. [2]Chawner SJRA Genotype-phenotype associations in children with copy number variants associated with high neuropsychiatric risk in the UK (IMAGINE-ID): a case-control cohort study Lancet Psychiatry 2019 6 493 505 10.1016/S2215-0366(19)30123-3 31056457 · DOI
  2. [3]Niarchou M Psychiatric disorders in children with 16p11.2 deletion and duplication Transl. Psychiatry 2019 9 8 10.1038/s41398-018-0339-8 30664628 PMC6341088 · DOI · Paper page
  3. [4]Friston K Brown HR Siemerkus J Stephan KE The dysconnection hypothesis Schizophr. Res. 2016 176 83 94 10.1016/j.schres.2016.07.014 27450778 PMC5147460 · DOI · Paper page
  4. [5]Dong D Wang Y Chang X Luo C Yao D Dysfunction of large-scale brain networks in schizophrenia: a meta-analysis of resting-state functional connectivity Schizophr. Bull. 2018 44 168 181 10.1093/schbul/sbx034 28338943 PMC5767956 · DOI · Paper page
  5. [6]Hull JV Resting-state functional connectivity in autism spectrum disorders: a review Front. Psychiatry 2017 7 205 10.3389/fpsyt.2016.00205 28101064 PMC5209637 · DOI · Paper page
  6. [7]O’Reilly C Lewis JD Elsabbagh M Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies PLoS ONE 2017 12 e0175870 10.1371/journal.pone.0175870 28467487 PMC5414938 · DOI
  7. [8]Sha Z Wager TD Mechelli A He Y Common dysfunction of large-scale neurocognitive networks across psychiatric disorders Biol. Psychiatry 2019 85 379 388 10.1016/j.biopsych.2018.11.011 30612699 · DOI · Paper page
  8. [10]Jonas RK Montojo CA Bearden CE The 22q11.2 deletion syndrome as a window into complex neuropsychiatric disorders over the lifespan Biol. Psychiatry 2014 75 351 360 10.1016/j.biopsych.2013.07.019 23992925 PMC3875621 · DOI
  9. [11]Schneider M Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on brain and behavior in 22q11.2 deletion syndrome Am. J. Psychiatry 2014 171 627 639 10.1176/appi.ajp.2013.13070864 24577245 PMC4285461 · DOI · Paper page
  10. [12]Niarchou M Psychopathology and cognition in children with 22q11.2 deletion syndrome Br. J. Psychiatry 2014 204 46 54 10.1192/bjp.bp.113.132324 24115343 PMC3877833 · DOI
  11. [13]Owen MJ Doherty JL What can we learn from the high rates of schizophrenia in people with 22q11.2 deletion syndrome? World Psychiatry 2016 15 23 25 10.1002/wps.20274 26833600 PMC4780289 · DOI
  12. [14]Zinkstok J van Amelsvoort TAMJ Neuropsychological profile and neuroimaging in patients with 22Q11.2 deletion syndrome: a review Child Neuropsychol. 2005 11 21 37 10.1080/09297040590911194 15823981 · DOI
  13. [15]Boot E van Amelsvoort TAMJ Neuroimaging correlates of 22q11.2 deletion syndrome: implications for schizophrenia research Curr. Top. Med. Chem. 2013 12 2303 2313 10.2174/1568026611212210003 23279171 · DOI
  14. [16]Reddaway JT Genomic and imaging biomarkers in schizophrenia Curr. Top. Behav. Neurosci. 2018 40 325 352 10.1007/7854_2018_52 29696604 · DOI
  15. [17]Sun D Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: convergence with idiopathic psychosis and effects of deletion size Mol. Psychiatry 2020 25 1822 1834 10.1038/s41380-018-0078-5 29895892 PMC6292748 · DOI
  16. [18]Ottet M-C Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations Front. Hum. Neurosci. 2013 7 402 10.3389/fnhum.2013.00402 24046733 PMC3763187 · DOI
  17. [19]Villalón-Reina, J. E. et al. Altered white matter microstructure in 22q11. 2 deletion syndrome: a multisite diffusion tensor imaging study. Mol. Psychiatry 1–14 (2019). PMC6986984 31358905
  18. [20]Debbané M Resting-state networks in adolescents with 22q11.2 deletion syndrome: associations with prodromal symptoms and executive functions Schizophr. Res. 2012 139 33 39 10.1016/j.schres.2012.05.021 22704643 · DOI
  19. [21]Padula MC Structural and functional connectivity in the default mode network in 22q11.2 deletion syndrome J. Neurodev. Disord. 2015 7 23 10.1186/s11689-015-9120-y 26236404 PMC4522079 · DOI
  20. [22]Scariati E Large-scale functional network reorganization in 22q11.2 deletion syndrome revealed by modularity analysis Cortex 2016 82 86 99 10.1016/j.cortex.2016.06.004 27371790 · DOI
  21. [23]Tomescu MI Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: a vulnerability marker of schizophrenia? Schizophr. Res. 2014 157 175 181 10.1016/j.schres.2014.05.036 24962438 · DOI
  22. [24]Berman JI Relationship between M100 auditory evoked response and auditory radiation microstructure in 16p11.2 deletion and duplication carriers Am. J. Neuroradiol. 2016 37 1178 1184 10.3174/ajnr.A4687 26869473 PMC4907815 · DOI
  23. [25]Drakesmith M Genetic risk for schizophrenia and developmental delay is associated with shape and microstructure of midline white-matter structures Transl. Psychiatry 2019 9 102 10.1038/s41398-019-0440-7 30804328 PMC6389944 · DOI · Paper page
  24. [26]Silva AI Reciprocal white matter changes associated with copy number variation at 15q11.2 BP1-BP2: a diffusion tensor imaging study Biol. Psychiatry 2019 85 563 572 10.1016/j.biopsych.2018.11.004 30583851 PMC6424871 · DOI · Paper page
  25. [27]Frohlich J A quantitative electrophysiological biomarker of duplication 15q11.2-q13.1 syndrome PLoS ONE 2016 11 e0167179 10.1371/journal.pone.0167179 27977700 PMC5157977 · DOI
  26. [28]Frohlich J Mechanisms underlying the EEG biomarker in Dup15q syndrome Mol. Autism 2019 10 29 10.1186/s13229-019-0280-6 31312421 PMC6609401 · DOI
  27. [29]Hinkley LBN Sensorimotor cortical oscillations during movement preparation in 16p11.2 deletion carriers J. Neurosci. 2019 39 7321 7331 10.1523/JNEUROSCI.3001-17.2019 31270155 PMC6759026 · DOI
  28. [30]Jenkins J Auditory evoked M100 response latency is delayed in children with 16p11.2 deletion but not 16p11.2 duplication Cereb. Cortex 2016 26 1957 1964 10.1093/cercor/bhv008 25678630 PMC6276914 · DOI
  29. [31]Moss S Validity of the PAS-ADD for detecting psychiatric symptoms in adults with learning disability (mental retardation) Soc. Psychiatry Psychiatr. Epidemiol. 1997 32 344 354 10.1007/BF00805440 9299929 · DOI
  30. [32]Miller TJ Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability Schizophr. Bull. 2003 29 703 715 10.1093/oxfordjournals.schbul.a007040 14989408 · DOI
  31. [33]First, M. B. & Gibbon, M. In Comprehensive Handbook of Psychological Assessment, Vol. 2. Personality Assessment (eds. Hilsenroth, M. J. & Segal, D. L.). 134–143 (Wiley, Hoboken, NJ, 2004).
  32. [34]Vrba J Magnetoencephalography: the art of finding a needle in a haystack Phys. C 2002 368 1 9 10.1016/S0921-4534(01)01131-5 · DOI
  33. [35]Oostenveld R Fries P Maris E Schoffelen JM FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data Comput. Intell. Neurosci. 2011 2011 156869 10.1155/2011/156869 21253357 PMC3021840 · DOI · Paper page
  34. [36]Colclough GL How reliable are MEG resting-state connectivity metrics? Neuroimage 2016 138 284 293 10.1016/j.neuroimage.2016.05.070 27262239 PMC5056955 · DOI
  35. [37]Huang MX Mosher JC Leahy RM A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG Phys. Med. Biol. 1999 44 423 440 10.1088/0031-9155/44/2/010 10070792 · DOI
  36. [38]Hillebrand A Barnes GR Bosboom JL Berendse HW Stam CJ Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution Neuroimage 2012 59 3909 3921 10.1016/j.neuroimage.2011.11.005 22122866 PMC3382730 · DOI · Paper page
  37. [39]Tzourio-Mazoyer N Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain Neuroimage 2002 15 273 289 10.1006/nimg.2001.0978 11771995 · DOI · Paper page
  38. [40]Colclough GL Brookes MJ Smith SM Woolrich MW A symmetric multivariate leakage correction for MEG connectomes Neuroimage 2015 117 439 448 10.1016/j.neuroimage.2015.03.071 25862259 PMC4528074 · DOI
  39. [41]Siems M Pape A-A Hipp JF Siegel M Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG Neuroimage 2016 129 345 355 10.1016/j.neuroimage.2016.01.055 26827813 · DOI
  40. [43]Sønderby, I. E. et al. Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia. Mol Psychiatry.10.1038/s41380-018-0118-1(2018). PMC7042770 30283035 · DOI
  41. [44]Warland, A., Kendall, K. M., Rees, E., Kirov, G. & Caseras, X. Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank. Mol. Psychiatry25, 854–862 (2019). PMC7156345 30679740
  42. [45]Koelewijn L Oscillatory hyperactivity and hyperconnectivity in young APOE-ɛ4 carriers and hypoconnectivity in Alzheimer’s disease Elife 2019 8 1 25 10.7554/eLife.36011 PMC6491037 31038453 · DOI
  43. [46]Nichols TE Holmes AP Nonparametric permutation tests for functional neuroimaging: a primer with examples Hum. Brain Mapp. 2001 25 1 25 PMC6871862 11747097
  44. [47]Rubinov M Sporns O Complex network measures of brain connectivity: uses and interpretations Neuroimage 2010 52 1059 1069 10.1016/j.neuroimage.2009.10.003 19819337 · DOI · Paper page
  45. [48]Boser, B. E., Guyon, I. M. & Vapnik, V. N. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory—COLT ’92 (ACM Press, New York, 1992). pp. 144–152.
  46. [49]Schnitzler A Gross J Normal and pathological oscillatory communication in the brain Nat. Rev. Neurosci. 2005 6 285 296 10.1038/nrn1650 15803160 · DOI
  47. [50]Brookes MJ A multi-layer network approach to MEG connectivity analysis Neuroimage 2016 132 425 438 10.1016/j.neuroimage.2016.02.045 26908313 PMC4862958 · DOI
  48. [51]Phalen, H., Coffman, B. A., Ghuman, A., Sejdić, E. & Salisbury, D. F. Non-negative matrix factorization reveals resting-state cortical alpha network abnormalities in the first episode schizophrenia-spectrum. Biol. Psychiatry Cogn. Neurosci. Neuroimaging (2019). PMC6943199 31451387
  49. [52]Lewis JD Network inefficiencies in autism spectrum disorder at 24 months Transl. Psychiatry 2014 4 e388 e388 10.1038/tp.2014.24 24802306 PMC4035719 · DOI · Paper page
  50. [53]Scariati E Padula MC Schaer M Eliez S Long-range dysconnectivity in frontal and midline structures is associated to psychosis in 22q11.2 deletion syndrome J. Neural Transm. 2016 123 823 839 10.1007/s00702-016-1548-z 27094177 · DOI
  51. [54]Schreiner MJ Default mode network connectivity and reciprocal social behavior in 22q11.2 deletion syndrome Soc. Cogn. Affect. Neurosci. 2014 9 1261 1267 10.1093/scan/nst114 23912681 PMC4158365 · DOI
  52. [55]Larsen KM Dzafic I Siebner HR Garrido MI Alteration of functional brain architecture in 22q11.2 deletion syndrome—insights into susceptibility for psychosis Neuroimage 2019 190 154 171 10.1016/j.neuroimage.2018.09.001 30195053 · DOI
  53. [56]Deco G How local excitation-inhibition ratio impacts the whole brain dynamics J. Neurosci. 2014 34 7886 7898 10.1523/JNEUROSCI.5068-13.2014 24899711 PMC4044249 · DOI
  54. [57]Gu H Hu Y Chen X He Y Yang Y Regional excitation-inhibition balance predicts default-mode network deactivation via functional connectivity Neuroimage 2019 185 388 397 10.1016/j.neuroimage.2018.10.055 30359729 PMC8284909 · DOI
  55. [59]Lee E Lee J Kim E Excitation/inhibition imbalance in animal models of autism spectrum disorders Biol. Psychiatry 2017 81 838 847 10.1016/j.biopsych.2016.05.011 27450033 · DOI · Paper page
  56. [60]Foss-Feig JH Searching for cross-diagnostic convergence: neural mechanisms governing excitation and inhibition balance in schizophrenia and autism spectrum disorders Biol. Psychiatry 2017 81 848 861 10.1016/j.biopsych.2017.03.005 28434615 PMC5436134 · DOI
  57. [61]Kehrer C Maziashvili N Dugladze T Gloveli T Altered excitatory-inhibitory balance in the NMDA-hypofunction model of schizophrenia Front. Mol. Neurosci. 2008 1 6 10.3389/neuro.02.006.2008 18946539 PMC2525998 · DOI · Paper page
  58. [62]Ramamoorthi K Lin Y The contribution of GABAergic dysfunction to neurodevelopmental disorders Trends Mol. Med. 2011 17 452 462 10.1016/j.molmed.2011.03.003 21514225 PMC3152662 · DOI
  59. [63]Pocklington AJ Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia Neuron 2015 86 1203 1214 10.1016/j.neuron.2015.04.022 26050040 PMC4460187 · DOI · Paper page
  60. [65]Sandini C Cortical dysconnectivity measured by structural covariance is associated with the presence of psychotic symptoms in 22q11.2 deletion syndrome Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2018 3 433 442 29735153

Cited by

Sorted by citations

Authors
Dima, D. C., Adams, R., Linden, S. C., Baird, A., Smith, J., Foley, S., Perry, G., Routley, B. C., Magazzini, L., Drakesmith, M., Williams, N., Doherty, J., van den Bree, M. B. M., Owen, M. J., Hall, J., Linden, D. E. J., & Singh, K. D.
Affiliations
  • Cardiff University (GB)
  • Maastricht University (NL)
Corresponding author
Dima, Diana C.
ORCID
Venue
Translational Psychiatry
Publisher
Springer Science and Business Media LLC
Published
2020-09-21
Volume
10
Issue
1
Pages
324-324
ISSN
2158-3188 (ISSN-L)
DOI
10.1038/s41398-020-00998-w
PMC
PMC7506525
OpenAlex
W3087413063
Cited by
14
References
4
Citations by year
2026: 1 · 2025: 4 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 1
FWCI
1.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Translational Psychiatry (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion, cc-by)
  • ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
  • Research Publications (Maastricht University) (submittedVersion, cc-by)
Funders
  • Wellcome Trust (100202/Z/12/Z)
  • RCUK | Medical Research Council (MR/K501086/1, MR/K005464/1)
  • RCUK | MRC | Medical Research Foundation (MR/K501086/1, MR/K005464/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
Keywords
copy number variation, neurodevelopmental disorders, resting-state functional connectivity, neural integration, 22q11.2 deletion syndrome, graph theory, node centrality, network segregation, magnetoencephalography, psychiatric risk
MeSH terms
Adult, Mental Disorders, Genotype, Humans, Phenotype, Cohort Studies, Genetic Predisposition to Disease, DNA Copy Number Variations

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review