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Brain Topography

, Volume 27, Issue 6, pp 808–821 | Cite as

Identifying 22q11.2 Deletion Syndrome and Psychosis Using Resting-State Connectivity Patterns

  • Elisa Scariati
  • Marie Schaer
  • Jonas Richiardi
  • Maude Schneider
  • Martin Debbané
  • Dimitri Van De Ville
  • Stephan Eliez
Original Paper

Abstract

The clinical picture associated with 22q11.2 deletion syndrome (22q11DS) includes mild mental retardation and an increased risk of schizophrenia. While the clinical phenotype has been related to structural brain network alterations, there is only scarce information about functional connectivity in 22q11DS. However, such studies could lead to a better comprehension of the disease and reveal potential biomarkers for psychosis. A connectivity decoding approach was used to discriminate between 42 patients with 22q11DS and 41 controls using resting-state connectivity. The same method was then applied within the 22q11DS group to identify brain connectivity patterns specifically related to the presence of psychotic symptoms. An accuracy of 84 % was achieved in differentiating patients with 22q11DS from controls. The discriminative connections were widespread, but predominantly located in the bilateral frontal and right temporal lobes, and were significantly correlated to IQ. An 88 % accuracy was obtained for identification of existing psychotic symptoms within the patients group. The regions containing most discriminative connections included the anterior cingulate cortex (ACC), the left superior temporal and the right inferior frontal gyri. Functional connectivity alterations in 22q11DS affect mostly frontal and right temporal lobes and are related to the syndrome’s mild mental retardation. These results also provide evidence that resting-state connectivity can potentially become a biomarker for psychosis and that ACC plays an important role in the development of psychotic symptoms.

Keywords

22q11DS Resting-state networks Brain connectivity decoding Schizophrenia Early psychosis 

Notes

Acknowledgements

We would like to thank Ayca Karagöz Uzel, Laure Chevalley and Elodie Cuche for participating in data collection; Sophie Dahoun, Christine Hinard and Lucia Bartoloni for genetical analyses and Sarah Menghetti for her involvement with the families and proofreading the manuscript. Further thanks go to François Lazeyras, Frank Henry and Yohann Ouvrier Buffet at the CIBM. Finally we acknowledge the patients and their families who kindly participated in our study. This study was supported by the Swiss National Science Foundation (SNF) (Grant numbers: to S. Eliez 32473B_121996 and 234730_144260; to D. Van De Ville PP00P2_123438 and PP00P2_123438) and by the National Center of Competence in Research (NCCR) “Synapsy-The Synaptic Bases of Mental Diseases” (SNF, Grant number: 51AU40_125759). E. Scariati (#145250) and M. Schaer (#145760) were supported by a fellowship from the SNF and J. Richiardi by a Marie-Curie International Outgoing Fellowship (#299500).

Conflict of interest

The authors declare no conflicts of interest.

References

  1. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26(1):63–72. doi: 10.1523/JNEUROSCI.3874-05.2006 PubMedGoogle Scholar
  2. Alemán-Gómez YM-GL, Valdés-Hernandez P (2006) IBASPM: toolbox for automatic parcellation of brain structures. In: Paper presented at the presented at the 12th annual meeting of the organization for human brain mapping, Florence, Italy, June 11–15Google Scholar
  3. Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci. doi: 10.1038/nrn3465 PubMedPubMedCentralGoogle Scholar
  4. Allen P, Aleman A, McGuire PK (2007) Inner speech models of auditory verbal hallucinations: evidence from behavioural and neuroimaging studies. Int Rev Psychiatry 19(4):407–415. doi: 10.1080/09540260701486498 PubMedGoogle Scholar
  5. Allen P, Stephan KE, Mechelli A, Day F, Ward N, Dalton J, Williams SC, McGuire P (2010) Cingulate activity and fronto-temporal connectivity in people with prodromal signs of psychosis. Neuroimage 49(1):947–955. doi: 10.1016/j.neuroimage.2009.08.038 PubMedPubMedCentralGoogle Scholar
  6. Andersson F, Glaser B, Spiridon M, Debbane M, Vuilleumier P, Eliez S (2008) Impaired activation of face processing networks revealed by functional magnetic resonance imaging in 22q11.2 deletion syndrome. Biol Psychiatry 63(1):49–57. doi: 10.1016/j.biopsych.2007.02.022 PubMedGoogle Scholar
  7. Antshel KM, Fremont W, Kates WR (2008a) The neurocognitive phenotype in velo-cardio-facial syndrome: a developmental perspective. Dev Disabil Res Rev 14(1):43–51. doi: 10.1002/ddrr.7 PubMedGoogle Scholar
  8. Antshel KM, Peebles J, AbdulSabur N, Higgins AM, Roizen N, Shprintzen R, Fremont WP, Nastasi R, Kates WR (2008b) Associations between performance on the rey–osterrieth complex figure and regional brain volumes in children with and without velocardiofacial syndrome. Dev Neuropsychol 33(5):601–622. doi: 10.1080/87565640802254422 PubMedPubMedCentralGoogle Scholar
  9. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851. doi: 10.1016/j.neuroimage.2005.02.018 PubMedGoogle Scholar
  10. Baker KD, Skuse DH (2005) Adolescents and young adults with 22q11 deletion syndrome: psychopathology in an at-risk group. Br J Psychiatry 186:115–120. doi: 10.1192/bjp.186.2.115 PubMedGoogle Scholar
  11. Baker K, Vorstman JA (2012) Is there a core neuropsychiatric phenotype in 22q11.2 deletion syndrome? Curr Opin Neurol 25(2):131–137. doi: 10.1097/WCO.0b013e328352dd58 PubMedGoogle Scholar
  12. Barnea-Goraly N, Menon V, Krasnow B, Ko A, Reiss A, Eliez S (2003) Investigation of white matter structure in velocardiofacial syndrome: a diffusion tensor imaging study. Am J Psychiatry 160(10):1863–1869PubMedGoogle Scholar
  13. Barnea-Goraly N, Eliez S, Menon V, Bammer R, Reiss AL (2005) Arithmetic ability and parietal alterations: a diffusion tensor imaging study in velocardiofacial syndrome. Brain Res Cogn Brain Res 25(3):735–740. doi: 10.1016/j.cogbrainres.2005.09.013 PubMedGoogle Scholar
  14. Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO (2012) Altered resting state complexity in schizophrenia. Neuroimage 59(3):2196–2207. doi: 10.1016/j.neuroimage.2011.10.002 PubMedPubMedCentralGoogle Scholar
  15. Bearden CE, van Erp TG, Dutton RA, Tran H, Zimmermann L, Sun D, Geaga JA, Simon TJ, Glahn DC, Cannon TD, Emanuel BS, Toga AW, Thompson PM (2007) Mapping cortical thickness in children with 22q11.2 deletions. Cereb Cortex 17(8):1889–1898. doi: 10.1093/cercor/bhl097 PubMedPubMedCentralGoogle Scholar
  16. Bearden CE, van Erp TG, Dutton RA, Lee AD, Simon TJ, Cannon TD, Emanuel BS, McDonald-McGinn D, Zackai EH, Thompson PM (2009) Alterations in midline cortical thickness and gyrification patterns mapped in children with 22q11.2 deletions. Cereb Cortex 19(1):115–126. doi: 10.1093/cercor/bhn064 PubMedPubMedCentralGoogle Scholar
  17. Benjamini YHY (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300Google Scholar
  18. Borgwardt S, Koutsouleris N, Aston J, Studerus E, Smieskova R, Riecher-Rossler A, Meisenzahl EM (2013) Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophr Bull 39(5):1105–1114. doi: 10.1093/schbul/sbs095 PubMedPubMedCentralGoogle Scholar
  19. Brancucci A (2012) Neural correlates of cognitive ability. J Neurosci Res 90(7):1299–1309. doi: 10.1002/jnr.23045 PubMedGoogle Scholar
  20. Buckholtz JW, Meyer-Lindenberg A (2012) Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness. Neuron 74(6):990–1004. doi: 10.1016/j.neuron.2012.06.002 PubMedGoogle Scholar
  21. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198. doi: 10.1038/nrn2575 PubMedGoogle Scholar
  22. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intel Syst Technol 2(3):1–27. doi: 10.1145/1961189.1961199 Google Scholar
  23. Cole MW, Yarkoni T, Repovs G, Anticevic A, Braver TS (2012) Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J Neurosci 32(26):8988–8999. doi: 10.1523/JNEUROSCI.0536-12.2012 PubMedPubMedCentralGoogle Scholar
  24. Craddock RC, Holtzheimer PE 3rd, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619–1628. doi: 10.1002/mrm.22159 PubMedPubMedCentralGoogle Scholar
  25. Crossley NA, Mechelli A, Fusar-Poli P, Broome MR, Matthiasson P, Johns LC, Bramon E, Valmaggia L, Williams SC, McGuire PK (2009) Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis. Hum Brain Mapp 30(12):4129–4137. doi: 10.1002/hbm.20834 PubMedGoogle Scholar
  26. da Silva Alves F, Schmitz N, Bloemen O, van der Meer J, Meijer J, Boot E, Nederveen A, de Haan L, Linszen D, van Amelsvoort T (2011) White matter abnormalities in adults with 22q11 deletion syndrome with and without schizophrenia. Schizophr Res 132(1):75–83. doi: 10.1016/j.schres.2011.07.017 Google Scholar
  27. Davatzikos C, Shen D, Gur RC, Wu X, Liu D, Fan Y, Hughett P, Turetsky BI, Gur RE (2005) Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch Gen Psychiatry 62(11):1218–1227. doi: 10.1001/archpsyc.62.11.1218 PubMedGoogle Scholar
  28. Debbane M, Lazouret M, Lagioia A, Schneider M, Van De Ville D, Eliez S (2012) Resting-state networks in adolescents with 22q11.2 deletion syndrome: associations with prodromal symptoms and executive functions. Schizophr Res 139(1–3):33–39. doi: 10.1016/j.schres.2012.05.021 PubMedGoogle Scholar
  29. Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, Nelson SM, Wig GS, Vogel AC, Lessov-Schlaggar CN, Barnes KA, Dubis JW, Feczko E, Coalson RS, Pruett JR Jr, Barch DM, Petersen SE, Schlaggar BL (2010) Prediction of individual brain maturity using fMRI. Science 329(5997):1358–1361. doi: 10.1126/science.1194144 PubMedPubMedCentralGoogle Scholar
  30. Eliez S, Schmitt JE, White CD, Reiss AL (2000) Children and adolescents with velocardiofacial syndrome: a volumetric MRI study. Am J Psychiatry 157(3):409–415PubMedGoogle Scholar
  31. Filippi M, Agosta F (2011) Structural and functional network connectivity breakdown in Alzheimer’s disease studied with magnetic resonance imaging techniques. J Alzheimers Dis 24(3):455–474. doi: 10.3233/JAD-2011-101854 PubMedGoogle Scholar
  32. First MB (1997) Structured clinical interview for DSM-IV axis II personality disorders: SCID-II. American Psychiatric Press, WashingtonGoogle Scholar
  33. Fornito A, Harrison BJ, Goodby E, Dean A, Ooi C, Nathan PJ, Lennox BR, Jones PB, Suckling J, Bullmore ET (2013) Functional dysconnectivity of corticostriatal circuitry as a risk phenotype for psychosis. JAMA Psychiatry 70(11):1143–1151. doi: 10.1001/jamapsychiatry.2013.1976 PubMedGoogle Scholar
  34. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700–711. doi: 10.1038/nrn2201 PubMedGoogle Scholar
  35. Fryer SL, Woods SW, Kiehl KA, Calhoun VD, Pearlson GD, Roach BJ, Ford JM, Srihari VH, McGlashan TH, Mathalon DH (2013) Deficient suppression of default mode regions during working memory in individuals with early psychosis and at clinical high-risk for psychosis. Front Psychiatry 4:92. doi: 10.3389/fpsyt.2013.00092 PubMedPubMedCentralGoogle Scholar
  36. Fusar-Poli P, Borgwardt S, Bechdolf A, Addington J, Riecher-Rossler A, Schultze-Lutter F, Keshavan M, Wood S, Ruhrmann S, Seidman LJ, Valmaggia L, Cannon T, Velthorst E, De Haan L, Cornblatt B, Bonoldi I, Birchwood M, McGlashan T, Carpenter W, McGorry P, Klosterkotter J, McGuire P, Yung A (2013) The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry 70(1):107–120. doi: 10.1001/jamapsychiatry.2013.269 PubMedGoogle Scholar
  37. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD (2007) Aberrant “default mode” functional connectivity in schizophrenia. Am J Psychiatry 164(3):450–457. doi: 10.1176/appi.ajp.164.3.450 PubMedGoogle Scholar
  38. Gothelf D, Schaer M, Eliez S (2008) Genes, brain development and psychiatric phenotypes in velo-cardio-facial syndrome. Dev Disabil Res Rev 14(1):59–68. doi: 10.1002/ddrr.9 PubMedGoogle Scholar
  39. Gothelf D, Hoeft F, Ueno T, Sugiura L, Lee AD, Thompson P, Reiss AL (2011) Developmental changes in multivariate neuroanatomical patterns that predict risk for psychosis in 22q11.2 deletion syndrome. J Psychiatr Res 45(3):322–331. doi: 10.1016/j.jpsychires.2010.07.008 PubMedPubMedCentralGoogle Scholar
  40. Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21(4):424–430. doi: 10.1097/WCO.0b013e328306f2c5 PubMedGoogle Scholar
  41. Haxby JV, Hoffman EA, Gobbini MI (2000) The distributed human neural system for face perception. Trends Cogn Sci 4(6):223–233PubMedGoogle Scholar
  42. He Y, Chen ZJ, Evans AC (2007) Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 17(10):2407–2419. doi: 10.1093/cercor/bhl149 PubMedGoogle Scholar
  43. Honea R, Crow TJ, Passingham D, Mackay CE (2005) Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry 162(12):2233–2245. doi: 10.1176/appi.ajp.162.12.2233 PubMedGoogle Scholar
  44. Jung WH, Jang JH, Byun MS, An SK, Kwon JS (2010) Structural brain alterations in individuals at ultra-high risk for psychosis: a review of magnetic resonance imaging studies and future directions. J Korean Med Sci 25(12):1700–1709. doi: 10.3346/jkms.2010.25.12.1700 PubMedPubMedCentralGoogle Scholar
  45. Karbasforoushan H, Woodward ND (2012) Resting-state networks in schizophrenia. Curr Top Med Chem 12(21):2404–2414PubMedGoogle Scholar
  46. Karlsgodt KH, Niendam TA, Bearden CE, Cannon TD (2009) White matter integrity and prediction of social and role functioning in subjects at ultra-high risk for psychosis. Biol Psychiatry 66(6):562–569. doi: 10.1016/j.biopsych.2009.03.013 PubMedPubMedCentralGoogle Scholar
  47. Kates WR, Burnette CP, Jabs EW, Rutberg J, Murphy AM, Grados M, Geraghty M, Kaufmann WE, Pearlson GD (2001) Regional cortical white matter reductions in velocardiofacial syndrome: a volumetric MRI analysis. Biol Psychiatry 49(8):677–684PubMedGoogle Scholar
  48. Kates WR, Burnette CP, Bessette BA, Folley BS, Strunge L, Jabs EW, Pearlson GD (2004) Frontal and caudate alterations in velocardiofacial syndrome (deletion at chromosome 22q11.2). J Child Neurol 19(5):337–342PubMedGoogle Scholar
  49. Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Moller HJ, Gaser C (2009) Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry 66(7):700–712. doi: 10.1001/archgenpsychiatry.2009.62 PubMedPubMedCentralGoogle Scholar
  50. Koutsouleris N, Gaser C, Bottlender R, Davatzikos C, Decker P, Jager M, Schmitt G, Reiser M, Moller HJ, Meisenzahl EM (2010) Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis. Schizophr Res 123(2–3):175–187. doi: 10.1016/j.schres.2010.08.032 PubMedGoogle Scholar
  51. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Moller HJ, Riecher-Rossler A (2012) Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull 38(6):1234–1246. doi: 10.1093/schbul/sbr145 PubMedPubMedCentralGoogle Scholar
  52. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12(5):535–540. doi: 10.1038/nn.2303 PubMedPubMedCentralGoogle Scholar
  53. Lerch JP, Worsley K, Shaw WP, Greenstein DK, Lenroot RK, Giedd J, Evans AC (2006) Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage 31(3):993–1003. doi: 10.1016/j.neuroimage.2006.01.042 PubMedGoogle Scholar
  54. Li Y, Liu Y, Li J, Qin W, Li K, Yu C, Jiang T (2009) Brain anatomical network and intelligence. PLoS Comput Biol 5(5):e1000395. doi: 10.1371/journal.pcbi.1000395 PubMedPubMedCentralGoogle Scholar
  55. Lindsay EA, Goldberg R, Jurecic V, Morrow B, Carlson C, Kucherlapati RS, Shprintzen RJ, Baldini A (1995) Velo-cardio-facial syndrome: frequency and extent of 22q11 deletions. Am J Med Genet 57(3):514–522. doi: 10.1002/ajmg.1320570339 PubMedGoogle Scholar
  56. Lord LD, Allen P, Expert P, Howes O, Lambiotte R, McGuire P, Bose SK, Hyde S, Turkheimer FE (2011) Characterization of the anterior cingulate’s role in the at-risk mental state using graph theory. Neuroimage 56(3):1531–1539. doi: 10.1016/j.neuroimage.2011.02.012 PubMedGoogle Scholar
  57. Luck D, Buchy L, Czechowska Y, Bodnar M, Pike GB, Campbell JS, Achim A, Malla A, Joober R, Lepage M (2011) Fronto-temporal disconnectivity and clinical short-term outcome in first episode psychosis: a DTI-tractography study. J Psychiatr Res 45(3):369–377. doi: 10.1016/j.jpsychires.2010.07.007 PubMedGoogle Scholar
  58. Mazaika PHF, Glover GH, Reiss AL (2009) Methods and software for fMRI analysis for clinical subjects. In: Paper presented at the human brain mappingGoogle Scholar
  59. McGlashan TH, Miller TJ, Woods SW (2001) Structured interview for prodromal syndromes (SIPS; Version 3.0, unpublished manuscript). PRIME Research Clinic, Yale University, School of Medicine, New Heaven, ConneticutGoogle Scholar
  60. Menon V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci 15(10):483–506. doi: 10.1016/j.tics.2011.08.003 PubMedGoogle Scholar
  61. Meskaldji DE, Ottet MC, Cammoun L, Hagmann P, Meuli R, Eliez S, Thiran JP, Morgenthaler S (2011) Adaptive strategy for the statistical analysis of connectomes. PLoS One 6(8):e23009. doi: 10.1371/journal.pone.0023009 PubMedPubMedCentralGoogle Scholar
  62. Miller TJ, McGlashan TH, Rosen JL, Cadenhead K, Cannon T, Ventura J, McFarlane W, Perkins DO, Pearlson GD, Woods SW (2003) 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 29(4):703–715PubMedGoogle Scholar
  63. Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC (2009) Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry 66(8):811–822. doi: 10.1001/archgenpsychiatry.2009.91 PubMedPubMedCentralGoogle Scholar
  64. Modinos G, Pettersson-Yeo W, Allen P, McGuire PK, Aleman A, Mechelli A (2012) Multivariate pattern classification reveals differential brain activation during emotional processing in individuals with psychosis proneness. Neuroimage 59(3):3033–3041. doi: 10.1016/j.neuroimage.2011.10.048 PubMedGoogle Scholar
  65. Modinos G, Mechelli A, Pettersson-Yeo W, Allen P, McGuire P, Aleman A (2013) Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor. PeerJ 1:e42. doi: 10.7717/peerj.42 PubMedPubMedCentralGoogle Scholar
  66. Mourao-Miranda J, Reinders AA, Rocha-Rego V, Lappin J, Rondina J, Morgan C, Morgan KD, Fearon P, Jones PB, Doody GA, Murray RM, Kapur S, Dazzan P (2012) Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. Psychol Med 42(5):1037–1047. doi: 10.1017/S0033291711002005 PubMedPubMedCentralGoogle Scholar
  67. Murphy KC, Jones LA, Owen MJ (1999) High rates of schizophrenia in adults with velo-cardio-facial syndrome. Arch Gen Psychiatry 56(10):940–945PubMedGoogle Scholar
  68. Murray RJ, Schaer M, Debbane M (2012) Degrees of separation: a quantitative neuroimaging meta-analysis investigating self-specificity and shared neural activation between self- and other-reflection. Neurosci Biobehav Rev 36(3):1043–1059. doi: 10.1016/j.neubiorev.2011.12.013 PubMedGoogle Scholar
  69. Opris I, Bruce CJ (2005) Neural circuitry of judgment and decision mechanisms. Brain Res Brain Res Rev 48(3):509–526. doi: 10.1016/j.brainresrev.2004.11.001 PubMedGoogle Scholar
  70. Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A (2012) Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36(4):1140–1152. doi: 10.1016/j.neubiorev.2012.01.004 PubMedGoogle Scholar
  71. Ottet MC, Schaer M, Cammoun L, Schneider M, Debbane M, Thiran JP, Eliez S (2013a) Reduced fronto-temporal and limbic connectivity in the 22q11.2 deletion syndrome: vulnerability markers for developing schizophrenia? PLoS One 8(3):e58429. doi: 10.1371/journal.pone.0058429 PubMedPubMedCentralGoogle Scholar
  72. Ottet MC, Schaer M, Debbane M, Cammoun L, Thiran JP, Eliez S (2013b) Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations. Front Hum Neurosci 7:402. doi: 10.3389/fnhum.2013.00402 PubMedPubMedCentralGoogle Scholar
  73. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A (2011) Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev 35(5):1110–1124. doi: 10.1016/j.neubiorev.2010.11.004 PubMedGoogle Scholar
  74. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59(3):2142–2154. doi: 10.1016/j.neuroimage.2011.10.018 PubMedPubMedCentralGoogle Scholar
  75. Radoeva PD, Coman IL, Antshel KM, Fremont W, McCarthy CS, Kotkar A, Wang D, Shprintzen RJ, Kates WR (2012) Atlas-based white matter analysis in individuals with velo-cardio-facial syndrome (22q11.2 deletion syndrome) and unaffected siblings. Behav Brain Funct 8:38. doi: 10.1186/1744-9081-8-38 PubMedPubMedCentralGoogle Scholar
  76. Raizada RDS, Kriegeskorte N (2010) Pattern-information fMRI: new questions which it opens up and challenges which face it. Int J Imaging Syst Technol 20(1):31–41. doi: 10.1002/ima.v20:1 Google Scholar
  77. Reich W (2000) Diagnostic interview for children and adolescents (DICA). J Am Acad Child Adolesc Psychiatry 39(1):59–66. doi: 10.1097/00004583-200001000-00017 PubMedGoogle Scholar
  78. Richiardi J, Van De Ville D, Riesen K, Bunke H (2010) Vector space embedding of undirected graphs with fixed-cardinality vertex sequences for classification. In: Paper presented at the 20th international conference on pattern recognition (ICPR)Google Scholar
  79. Richiardi J, Eryilmaz H, Schwartz S, Vuilleumier P, Van De Ville D (2011) Decoding brain states from fMRI connectivity graphs. Neuroimage 56(2):616–626. doi: 10.1016/j.neuroimage.2010.05.081 PubMedGoogle Scholar
  80. Richiardi J, Gschwind M, Simioni S, Annoni JM, Greco B, Hagmann P, Schluep M, Vuilleumier P, Van De Ville D (2012) Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity. Neuroimage 62(3):2021–2033. doi: 10.1016/j.neuroimage.2012.05.078 PubMedGoogle Scholar
  81. Richiardi J, Achard S, Bunke H, Van De Ville D (2013) Machine learning with brain graphs: predictive modeling approaches for functional imaging in systems neuroscience. Sig Process Mag IEEE 30(3):58–70. doi: 10.1109/msp.2012.2233865 Google Scholar
  82. Rihs TA, Tomescu MI, Britz J, Rochas V, Custo A, Schneider M, Debbane M, Eliez S, Michel CM (2012) Altered auditory processing in frontal and left temporal cortex in 22q11.2 deletion syndrome: a group at high genetic risk for schizophrenia. Psychiatry. doi: 10.1016/j.pscychresns.2012.09.002 Google Scholar
  83. Ruhrmann S, Schultze-Lutter F, Salokangas RK, Heinimaa M, Linszen D, Dingemans P, Birchwood M, Patterson P, Juckel G, Heinz A, Morrison A, Lewis S, von Reventlow HG, Klosterkotter J (2010) Prediction of psychosis in adolescents and young adults at high risk: results from the prospective European prediction of psychosis study. Arch Gen Psychiatry 67(3):241–251. doi: 10.1001/archgenpsychiatry.2009.206 PubMedGoogle Scholar
  84. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, Wolf DH (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240–256. doi: 10.1016/j.neuroimage.2012.08.052 PubMedGoogle Scholar
  85. Schaer M, Debbane M, Bach Cuadra M, Ottet MC, Glaser B, Thiran JP, Eliez S (2009) Deviant trajectories of cortical maturation in 22q11.2 deletion syndrome (22q11DS): a cross-sectional and longitudinal study. Schizophr Res 115(2–3):182–190. doi: 10.1016/j.schres.2009.09.016 PubMedGoogle Scholar
  86. Schaer M, Glaser B, Ottet MC, Schneider M, Bach Cuadra M, Debbane M, Thiran JP, Eliez S (2010) Regional cortical volumes and congenital heart disease: a MRI study in 22q11.2 deletion syndrome. J Neurodev Disord 2(4):224–234. doi: 10.1007/s11689-010-9061-4 PubMedPubMedCentralGoogle Scholar
  87. Schmidt A, Smieskova R, Aston J, Simon A, Allen P, Fusar-Poli P, McGuire PK, Riecher-Rossler A, Stephan KE, Borgwardt S (2013) Brain connectivity abnormalities predating the onset of psychosis: correlation with the effect of medication. JAMA Psychiatry 70(9):903–912. doi: 10.1001/jamapsychiatry.2013.117 PubMedGoogle Scholar
  88. Schneider M, Debbane M, Lagioia A, Salomon R, d’Argembeau A, Eliez S (2012) Comparing the neural bases of self-referential processing in typically developing and 22q11.2 adolescents. Dev Cogn Neurosci 2(2):277–289. doi: 10.1016/j.dcn.2011.12.004 PubMedGoogle Scholar
  89. Schreiner MJ, Karlsgodt KH, Uddin LQ, Chow C, Congdon E, Jalbrzikowski M, Bearden CE (2013) Default mode network connectivity and reciprocal social behavior in 22q11.2 deletion syndrome. Soc Cogn Affect Neurosci. doi: 10.1093/scan/nst114 PubMedGoogle Scholar
  90. Shah J, Eack SM, Montrose DM, Tandon N, Miewald JM, Prasad KM, Keshavan MS (2012) Multivariate prediction of emerging psychosis in adolescents at high risk for schizophrenia. Schizophr Res 141(2–3):189–196. doi: 10.1016/j.schres.2012.08.012 PubMedPubMedCentralGoogle Scholar
  91. Shashi V, Kwapil TR, Kaczorowski J, Berry MN, Santos CS, Howard TD, Goradia D, Prasad K, Vaibhav D, Rajarethinam R, Spence E, Keshavan MS (2010) Evidence of gray matter reduction and dysfunction in chromosome 22q11.2 deletion syndrome. Psychiatry Res 181(1):1–8. doi: 10.1016/j.pscychresns.2009.07.003 PubMedPubMedCentralGoogle Scholar
  92. Shen H, Wang L, Liu Y, Hu D (2010) Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage 49(4):3110–3121. doi: 10.1016/j.neuroimage.2009.11.011 PubMedGoogle Scholar
  93. Shim G, Oh JS, Jung WH, Jang JH, Choi CH, Kim E, Park HY, Choi JS, Jung MH, Kwon JS (2010) Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study. Behav Brain Funct 6:58. doi: 10.1186/1744-9081-6-58 PubMedPubMedCentralGoogle Scholar
  94. Simon TJ, Ding L, Bish JP, McDonald-McGinn DM, Zackai EH, Gee J (2005) Volumetric, connective, and morphologic changes in the brains of children with chromosome 22q11.2 deletion syndrome: an integrative study. Neuroimage 25(1):169–180. doi: 10.1016/j.neuroimage.2004.11.018 PubMedGoogle Scholar
  95. Simon TJ, Wu Z, Avants B, Zhang H, Gee JC, Stebbins GT (2008) Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome. Behav Brain Funct 4:25. doi: 10.1186/1744-9081-4-25 PubMedPubMedCentralGoogle Scholar
  96. Sporns O, Tononi G, Kotter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1(4):e42. doi: 10.1371/journal.pcbi.0010042 PubMedPubMedCentralGoogle Scholar
  97. Sun J, Buys N (2012) Early executive function deficit in preterm children and its association with neurodevelopmental disorders in childhood: a literature review. Int J Adolesc Med Health 24(4):291–299. doi: 10.1515/ijamh.2012.042 PubMedGoogle Scholar
  98. Sundram F, Campbell LE, Azuma R, Daly E, Bloemen OJ, Barker GJ, Chitnis X, Jones DK, van Amelsvoort T, Murphy KC, Murphy DG (2010) White matter microstructure in 22q11 deletion syndrome: a pilot diffusion tensor imaging and voxel-based morphometry study of children and adolescents. J Neurodev Disord 2(2):77–92. doi: 10.1007/s11689-010-9043-6 PubMedPubMedCentralGoogle Scholar
  99. Tohka J, Foerde K, Aron AR, Tom SM, Toga AW, Poldrack RA (2008) Automatic independent component labeling for artifact removal in fMRI. Neuroimage 39(3):1227–1245. doi: 10.1016/j.neuroimage.2007.10.013 PubMedPubMedCentralGoogle Scholar
  100. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289PubMedGoogle Scholar
  101. van Amelsvoort T, Daly E, Robertson D, Suckling J, Ng V, Critchley H, Owen MJ, Henry J, Murphy KC, Murphy DG (2001) Structural brain abnormalities associated with deletion at chromosome 22q11: quantitative neuroimaging study of adults with velo-cardio-facial syndrome. Br J Psychiatry 178:412–419PubMedGoogle Scholar
  102. van Amelsvoort T, Schmitz N, Daly E, Deeley Q, Critchley H, Henry J, Robertson D, Owen M, Murphy KC, Murphy DG (2006) Processing facial emotions in adults with velo-cardio-facial syndrome: functional magnetic resonance imaging. Br J Psychiatry 189:560–561. doi: 10.1192/bjp.bp.105.019876 PubMedGoogle Scholar
  103. van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31(44):15775–15786. doi: 10.1523/JNEUROSCI.3539-11.2011 PubMedGoogle Scholar
  104. van den Heuvel MP, Stam CJ, Kahn RS, Pol Hulshoff HE (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29(23):7619–7624. doi: 10.1523/JNEUROSCI.1443-09.2009 PubMedGoogle Scholar
  105. Van Dijk KR, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59(1):431–438. doi: 10.1016/j.neuroimage.2011.07.044 PubMedPubMedCentralGoogle Scholar
  106. Van Overwalle F, Baetens K (2009) Understanding others’ actions and goals by mirror and mentalizing systems: a meta-analysis. Neuroimage 48(3):564–584. doi: 10.1016/j.neuroimage.2009.06.009 PubMedGoogle Scholar
  107. Venkataraman A, Whitford TJ, Westin CF, Golland P, Kubicki M (2012) Whole brain resting state functional connectivity abnormalities in schizophrenia. Schizophr Res 139(1–3):7–12. doi: 10.1016/j.schres.2012.04.021 PubMedPubMedCentralGoogle Scholar
  108. Vercammen A, Knegtering H, den Boer JA, Liemburg EJ, Aleman A (2010) Auditory hallucinations in schizophrenia are associated with reduced functional connectivity of the temporo-parietal area. Biol Psychiatry 67(10):912–918. doi: 10.1016/j.biopsych.2009.11.017 PubMedGoogle Scholar
  109. Volz KG, Schubotz RI, von Cramon DY (2006) Decision-making and the frontal lobes. Curr Opin Neurol 19(4):401–406. doi: 10.1097/01.wco.0000236621.83872.71 PubMedGoogle Scholar
  110. Wechsler D (ed) (1991) Wechsler Intelligence Scale for Children. Manual, 3rd edn. Psychological Corporation, San AntonioGoogle Scholar
  111. Wechsler D (ed) (1997) Wechsler adult intelligence scale. Administration and scoring manual, 3rd edn. Psychological Corporation, San AntonioGoogle Scholar
  112. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, Shenton ME, Green AI, Nieto-Castanon A, LaViolette P, Wojcik J, Gabrieli JD, Seidman LJ (2009) Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci USA 106(4):1279–1284. doi: 10.1073/pnas.0809141106 PubMedPubMedCentralGoogle Scholar
  113. Wildgruber D, Ackermann H, Kreifelts B, Ethofer T (2006) Cerebral processing of linguistic and emotional prosody: fMRI studies. Prog Brain Res 156:249–268. doi: 10.1016/S0079-6123(06)56013-3 PubMedGoogle Scholar
  114. Wu K, Taki Y, Sato K, Hashizume H, Sassa Y, Takeuchi H, Thyreau B, He Y, Evans AC, Li X, Kawashima R, Fukuda H (2013) Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence. PLoS One 8(2):e55347. doi: 10.1371/journal.pone.0055347 PubMedPubMedCentralGoogle Scholar
  115. Yung AR, Nelson B, Stanford C, Simmons MB, Cosgrave EM, Killackey E, Phillips LJ, Bechdolf A, Buckby J, McGorry PD (2008) Validation of “prodromal” criteria to detect individuals at ultra high risk of psychosis: 2 year follow-up. Schizophr Res 105(1–3):10–17. doi: 10.1016/j.schres.2008.07.012 PubMedGoogle Scholar
  116. Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53(4):1197–1207. doi: 10.1016/j.neuroimage.2010.06.041 PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Elisa Scariati
    • 1
  • Marie Schaer
    • 1
    • 2
  • Jonas Richiardi
    • 3
    • 4
  • Maude Schneider
    • 1
  • Martin Debbané
    • 1
    • 5
  • Dimitri Van De Ville
    • 6
    • 7
  • Stephan Eliez
    • 1
    • 8
  1. 1.Office Médico-Pédagogique Research Unit, Department of PsychiatryUniversity of Geneva School of MedicineGeneva 8Switzerland
  2. 2.Stanford Cognitive and Systems Neuroscience LaboratoryStanford University School of MedicinePalo AltoUSA
  3. 3.Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences and Department of Clinical NeurologyUniversity of GenevaGenevaSwitzerland
  4. 4.Functional Imaging in Neuropsychiatric Disorders Laboratory, Department of Neurology and Neurological SciencesStanford UniversityStanfordUSA
  5. 5.Adolescence Clinical Psychology Research Unit, Faculty of Psychology and Educational SciencesUniversity of GenevaGenevaSwitzerland
  6. 6.Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
  7. 7.Medical Image Processing Lab, Institute of BioengineeringEcole Polytechnique Féderale de LausanneLausanneSwitzerland
  8. 8.Department of Genetic Medicine and DevelopmentUniversity of Geneva School of MedicineGenevaSwitzerland

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