Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression

Abstract

The functional connectivity between thalamic medio-dorsal nucleus (MD) and cortical regions, especially the dorsolateral prefrontal cortex (DLPFC), is implicated in attentional processing and is anomalous in schizophrenia, a brain disease associated with polygenic risk and attentional deficits. However, the molecular and genetic underpinnings of thalamic connectivity anomalies are unclear. Given that gene co-expression across brain areas promotes synchronous interregional activity, our aim was to investigate whether coordinated expression of genes relevant to schizophrenia in MD and DLPFC may reflect thalamic connectivity anomalies in an attention-related network including the DLPFC. With this aim, we identified in datasets of post-mortem prefrontal mRNA expression from healthy controls a gene module with robust overrepresentation of genes with coordinated MD-DLPFC expression and enriched for schizophrenia genes according to the largest genome-wide association study to date. To link this gene cluster with imaging phenotypes, we computed a Polygenic Co-Expression Index (PCI) combining single-nucleotide polymorphisms predicting module co-expression. Finally, we investigated the association between PCI and thalamic functional connectivity during attention through fMRI Independent Component Analysis in 265 healthy participants. We found that PCI was positively associated with connectivity strength of a thalamic region overlapping with the MD within an attention brain circuit. These findings identify a novel association between schizophrenia-related genes and thalamic functional connectivity. Furthermore, they highlight the association between gene expression co-regulation and brain connectivity, such that genes with coordinated MD-DLPFC expression are associated with coordinated activity between the same brain regions. We suggest that gene co-expression is a plausible mechanism underlying biological phenotypes of schizophrenia.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. Andreasen NC, Paradiso S, O’Leary DS (1998) Cognitive dysmetria as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? Schizophr Bull 24(2):203–218

    CAS  PubMed  Article  Google Scholar 

  2. Antonucci LA, Taurisano P, Fazio L, Gelao B, Romano R, Quarto T, Porcelli A, Mancini M, Di Giorgio A, Caforio G, Pergola G, Popolizio T, Bertolino A, Blasi G (2016) Association of familial risk for schizophrenia with thalamic and medial prefrontal functional connectivity during attentional control. Schizophr Res 173(1–2):23–29

    PubMed  Article  Google Scholar 

  3. Antonucci LA, Bertolino A, Blasi G (2017) 3.0 T fMRI in psychiatry. In: Scarabino T, Pollice S, Popolizio T, editors. High field brain MRI: use in clinical practice. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-44174-0

    Google Scholar 

  4. Basak A, Hancarova M, Ulirsch JC, Balci TB, Trkova M, Pelisek M, Vlckova M, Muzikova K, Cermak J, Trka J, Dyment DA, Orkin SH, Daly MJ, Sedlacek Z, Sankaran VG (2015) BCL11A deletions result in fetal hemoglobin persistence and neurodevelopmental alterations. J Clin Investig 125(6):2363–2368

    PubMed  Article  Google Scholar 

  5. Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Bertolino A, Blasi G (2009) The genetics of schizophrenia. Neuroscience 164(1):288–299

    CAS  PubMed  Article  Google Scholar 

  7. Blasi G, Mattay VS, Bertolino A, Elvevåg B, Callicott JH, Das S, Kolachana BS, Egan MF, Goldberg TE, Weinberger DR (2005) Effect of catechol-O-methyltransferase val158met genotype on attentional control. J Neurosci 25(20):5038–5045

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Blasi G, Taurisano P, Papazacharias A, Caforio G, Romano R, Lobianco L, Fazio L, Di Giorgio A, Latorre V, Sambataro F, Popolizio T, Nardini M, Mattay VS, Weinberger DR, Bertolino A (2010) Nonlinear response of the anterior cingulate and prefrontal cortex in schizophrenia as a function of variable attentional control. Cereb Cortex 20(4):837–845

    PubMed  Article  Google Scholar 

  9. Calhoun VD, Kiehl KA, Pearlson GD (2008) Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum Brain Mapp 29(7):828–838

    PubMed  PubMed Central  Article  Google Scholar 

  10. Calhoun VD, Liu J, Adali T (2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(1 Suppl):S163–S172

    PubMed  Article  Google Scholar 

  11. Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM, Weinberger DR, Kleinman JE (2011) Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478(7370):519–523

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. de Leeuw N, Pfundt R, Koolen DA, Neefs I, Scheltinga I, Mieloo H, Sistermans EA, Nillesen W, Smeets DF, de Vries BB, Knoers NV (2008) A newly recognised microdeletion syndrome involving 2p15p16.1: narrowing down the critical region by adding another patient detected by genome wide tiling path array comparative genomic hybridisation analysis. J Med Genet 45(2):122

    PubMed  Article  Google Scholar 

  13. Fazio L, Pergola G, Papalino M, Di Carlo P, Monda A, Gelao B, Amoroso N, Tangaro S, Rampino A, Popolizio T, Bertolino A, Blasi G (2018) Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory. Proc Natl Acad Sci USA 7:201717135 (Epub ahead of print)

    Google Scholar 

  14. First MB, Gibbon M, Spitzer RL, Williams JBW (1996) Guide for the structured clinical interview for DSM-IV axis I disorders-research version. Biometrics Research Group, New York

    Google Scholar 

  15. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, Ruderfer DM, Oh EC, Topol A, Shah HR, Klei LL, Kramer R, Pinto D, Gümüş ZH, Cicek AE, Dang KK, Browne A, Lu C, Xie L, Readhead B, Stahl EA, Xiao J, Parvizi M, Hamamsy T, Fullard JF, Wang YC, Mahajan MC, Derry JM, Dudley JT, Hemby SE, Logsdon BA, Talbot K, Raj T, Bennett DA, De Jager PL, Zhu J, Zhang B, Sullivan PF, Chess A, Purcell SM, Shinobu LA, Mangravite LM, Toyoshiba H, Gur RE, Hahn CG, Lewis DA, Haroutunian V, Peters MA, Lipska BK, Buxbaum JD, Schadt EE, Hirai K, Roeder K, Brennand KJ, Katsanis N, Domenici E, Devlin B, Sklar P (2016) Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 19(11):1442–1453

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Gaiteri C, Ding Y, French B, Tseng GC, Sibille E (2014) Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 13(1):13–24

    CAS  PubMed  Article  Google Scholar 

  17. Giraldo-Chica M, Woodward ND (2017) Review of thalamocortical resting-state fMRI studies in schizophrenia. Schizophr Res 180:58–63

    PubMed  Article  Google Scholar 

  18. Gottesman II, Shields J (1971) Schizophrenia: geneticism and environmentalism. Hum Hered 21:517–522

    CAS  PubMed  Article  Google Scholar 

  19. Gunnersen JM, Kim MH, Fuller SJ, De Silva M, Britto JM, Hammond VE, Davies PJ, Petrou S, Faber ES, Sah P, Tan SS (2007) Sez-6 proteins affect dendritic arborization patterns and excitability of cortical pyramidal neurons. Neuron 56(4):621–639

    CAS  PubMed  Article  Google Scholar 

  20. Hinney A, Scherag A, Jarick I, Albayrak Ö, Pütter C, Pechlivanis S, Dauvermann MR, Beck S, Weber H, Scherag S, Nguyen TT, Volckmar AL, Knoll N, Faraone SV, Neale BM, Franke B, Cichon S, Hoffmann P, Nöthen MM, Schreiber S, Jöckel KH, Wichmann HE, Freitag C, Lempp T, Meyer J, Gilsbach S, Herpertz-Dahlmann B, Sinzig J, Lehmkuhl G, Renner TJ, Warnke A, Romanos M, Lesch KP, Reif A, Schimmelmann BG, Hebebrand J, Psychiatric, GWAS Consortium: ADHD subgroup (2011) Genome-wide association study in German patients with attention deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 156B(8):888–897

    PubMed  Article  Google Scholar 

  21. Hollingshead AB, Redlich FC (1958) Social class and mental illness a community study. New York: John Wiley.

    Google Scholar 

  22. Johnson MR, Shkura K, Langley SR, Delahaye-Duriez A, Srivastava P, Hill WD, Rackham OJ, Davies G, Harris SE, Moreno-Moral A, Rotival M, Speed D, Petrovski S, Katz A, Hayward C, Porteous DJ, Smith BH, Padmanabhan S, Hocking LJ, Starr JM, Liewald DC, Visconti A, Falchi M, Bottolo L, Rossetti T, Danis B, Mazzuferi M, Foerch P, Grote A, Helmstaedter C, Becker AJ, Kaminski RM, Deary IJ, Petretto E (2015) Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease. Nat Neurosci 19(2):223–232

    PubMed  Article  CAS  Google Scholar 

  23. Kim DI, Manoach DS, Mathalon DH, Turner JA, Mannell M, Brown GG, Ford JM, Gollub RL, White T, Wible C, Belger A, Bockholt HJ, Clark VP, Lauriello J, O’Leary D, Mueller BA, Lim KO, Andreasen N, Potkin SG, Calhoun VD (2009) Dysregulation of working memory and default-mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study. Hum Brain Mapp 30(11):3795–3811

    PubMed  PubMed Central  Article  Google Scholar 

  24. Krauth A, Blanc R, Poveda A, Jeanmonod D, Morel A, Székely G (2010) A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage 49(3):2053–2062

    PubMed  Article  Google Scholar 

  25. Kumari S, Nie J, Chen HS, Ma H, Stewart R, Li X, Lu M, Taylor WM, Wei H (2012) Evaluation of gene association methods for coexpression network construction and biological knowledge discovery. PLoS One 7:e50411

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Magri C, Sacchetti E, Traversa M, Valsecchi P, Gardella R, Bonvicini C, Minelli A, Gennarelli M, Barlati S (2010) New copy number variations in schizophrenia. PLoS One 5(10):e13422

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. Malhotra D, McCarthy S, Michaelson JJ, Vacic V, Burdick KE, Yoon S, Cichon S, Corvin A, Gary S, Gershon ES, Gill M, Karayiorgou M, Kelsoe JR, Krastoshevsky O, Krause V, Leibenluft E, Levy DL, Makarov V, Bhandari A, Malhotra AK, McMahon FJ, Nöthen MM, Potash JB, Rietschel M, Schulze TG, Sebat J (2011) High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72(6):951–963

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Matsui T, Sekiguchi M, Hashimoto A, Tomita U, Nishikawa T, Wada K (1995) Functional comparison of D-serine and glycine in rodents: the effect on cloned NMDA receptors and the extracellular concentration. J Neurochem 65(1):454–458

    CAS  PubMed  Article  Google Scholar 

  29. McKeown MJ, Sejnowski TJ (1998) Independent component analysis of fMRI data: examining the assumptions. Hum Brain Mapp 6(5–6):368–372

    CAS  PubMed  Article  Google Scholar 

  30. Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium, O’Dushlaine C, Rossin L, Lee PH, Holmans PA, Breen G (2015) Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci. 18:199–209

    Article  CAS  Google Scholar 

  31. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1):97–113

    CAS  PubMed  Article  Google Scholar 

  32. Pakkenberg B, Scheel-Kruger J, Kristiansen LV (2009) Schizophrenia; from structure to function with special focus on the mediodorsal thalamic prefrontal loop. Acta Psychiatr Scand 120:345–354

    CAS  PubMed  Article  Google Scholar 

  33. Pergola G, Güntürkün O, Koch B, Schwarz M, Daum I, Suchan B (2012) Recall deficits in stroke patients with thalamic lesions covary with damage to the parvocellular mediodorsal nucleus of the thalamus. Neuropsychologia 50(10):2477–2491

    PubMed  Article  Google Scholar 

  34. Pergola G, Selvaggi P, Trizio S, Bertolino A, Blasi G (2015) The role of the thalamus in schizophrenia from a neuroimaging perspective. Neurosci Biobehav Rev 54:57–75

    PubMed  Article  Google Scholar 

  35. Pergola G, Di Carlo P, Andriola I, Gelao B, Torretta S, Attrotto MT, Fazio L, Raio A, Albergo D, Masellis R, Rampino A, Blasi G, Bertolino A (2016) Combined effect of genetic variants in the GluN2B coding gene (GRIN2B) on prefrontal function during working memory performance. Psychol Med 46(6):1135–1150

    CAS  PubMed  Article  Google Scholar 

  36. Pergola G, Di Carlo P, D’Ambrosio E, Gelao B, Fazio L, Papalino M, Monda A, Scozia G, Pietrangelo B, Attrotto M, Apud JA, Chen Q, Mattay VS, Rampino A, Caforio G, Weinberger DR, Blasi G, Bertolino A (2017a) DRD2 co-expression network and a related polygenic index predict imaging, behavioral and clinical phenotypes linked to schizophrenia. Transl Psychiatry 7(1):e1006

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Pergola G, Trizio S, Di Carlo P, Taurisano P, Mancini M, Amoroso N, Nettis MA, Andriola I, Caforio G, Popolizio T, Rampino A, Di Giorgio A, Bertolino A, Blasi G (2017b) Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophr Res 180:13–20

    PubMed  Article  Google Scholar 

  38. Pergola G, Danet L, Pitel AL, Carlesimo GA, Segobin S, Pariente J, Suchan B, Mitchell AS, Barbeau EJ (2018) The regulatory role of the human mediodorsal thalamus. Trends Cogn Sci 22:1011–1025

    PubMed  PubMed Central  Article  Google Scholar 

  39. Rampino A, Di Carlo P, Fazio L, Ursini G, Pergola G, De Virgilio C, Gadaleta G, Giordano GM, Bertolino A, Blasi G (2017a) Association of functional genetic variation in PP2A with prefrontal working memory processing. Behav Brain Res 316:125–130

    CAS  PubMed  Article  Google Scholar 

  40. Rampino A, Taurisano P, Fanelli G, Attrotto M, Torretta S, Antonucci LA, Miccolis G, Pergola G, Ursini G, Maddalena G, Romano R, Masellis R, Di Carlo P, Pignataro P, Blasi G, Bertolino A (2017b) A Polygenic Risk Score of glutamatergic SNPs associated with schizophrenia predicts attentional behavior and related brain activity in healthy humans. Eur Neuropsychopharmacol. 27(9):928–939

    CAS  PubMed  Article  Google Scholar 

  41. Richiardi J, Altmann A, Milazzo AC, Chang C, Chakravarty MM, Banaschewski T, Barker GJ, Bokde AL, Bromberg U, Büchel C, Conrod P, Fauth-Bühler M, Flor H, Frouin V, Gallinat J, Garavan H, Gowland P, Heinz A, Lemaître H, Mann KF, Martinot JL, Nees F, Paus T, Pausova Z, Rietschel M, Robbins TW, Smolka MN, Spanagel R, Ströhle A, Schumann G, Hawrylycz M, Poline JB, Greicius MD, IMAGEN consortium (2015) BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348(6240):1241–1244

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Sambataro F, Blasi G, Fazio L, Caforio G, Taurisano P, Romano R, Di Giorgio A, Gelao B, Lo Bianco L, Papazacharias A, Popolizio T, Nardini M, Bertolino A (2010) Treatment with olanzapine is associated with modulation of the default mode network in patients with schizophrenia. Neuropsychopharmacology 35(4):904–912

    CAS  PubMed  Article  Google Scholar 

  43. Sankaran VG, Xu J, Ragoczy T, Ippolito GC, Walkley CR, Maika SD, Fujiwara Y, Ito M, Groudine M, Bender MA, Tucker PW, Orkin SH (2009) Developmental and species-divergent globin switching are driven by BCL11A. Nature 460(7259):1093–1097

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427

    PubMed Central  Article  CAS  Google Scholar 

  45. Schmitt LI, Wimmer RD, Nakajima M, Happ M, Mofakham S, Halassa MM (2017) Thalamic amplification of cortical connectivity sustains attentional control. Nature 545(7653):219–223

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Selvaggi P, Pergola G, Gelao B, Di Carlo P, Nettis MA, Amico G, Fazio L, Rampino A, Sambataro F, Blasi G, Bertolino A (2018) Genetic variation of a DRD2 co-expression network is associated with changes in prefrontal function after D2 receptors stimulation. Cereb Cortex. https://doi.org/10.1093/cercor/bhy022 (Epub ahead of print)

    Article  Google Scholar 

  47. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423 and 623–656

    Google Scholar 

  48. Sim K, Cullen T, Ongur D, Heckers S (2006) Testing models of thalamic dysfunction in schizophrenia using neuroimaging. J Neural Transm 113(7):907–928

    CAS  PubMed  Article  Google Scholar 

  49. Smieskova R, Marmy J, Schmidt A, Bendfeldt K, Riecher-RÓ§ssler A, Walter M, Lang UE, Borgwardt S (2013) Do subjects at clinical high risk for psychosis differ from those with a genetic high risk? A systematic review of structural and functional brain abnormalities. Curr Med Chem 20(3):467–481

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Sodhi MS, Simmons M, McCullumsmith R, Haroutunian V, Meador-Woodruff JH (2011) Glutamatergic gene expression is specifically reduced in thalamocortical projecting relay neurons in schizophrenia. Biol Psychiatry 70(7):646–654

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Trabzuni D, Ryten M, Walker R, Smith C, Imran S, Ramasamy A, Weale ME, Hardy J (2011) Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J Neurochem 119(2):275–282

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Wechsler D (1981) WAIS-R manual: Wechsler adult intelligence scale-revised. Psychological Corporation: New York

    Google Scholar 

  53. Weinberger DR (1987) Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry 44(7):660–669

    CAS  PubMed  Article  Google Scholar 

  54. Wolosker H, Blackshaw S, Snyder SH (1999) Serine racemase: a glial enzyme synthesizing d-serine to regulate glutamate-N-methyl-d-aspartate neurotransmission. Proc Natl Acad Sci 96(23):13409–13414

    CAS  PubMed  Article  Google Scholar 

  55. Xu W, Hou Y, Hung YS, Zou Y (2010) Comparison of Spearman’s rho and Kendall’s tau in normal and contaminated normal models. Signal Process. https://doi.org/10.1016/j.sigpro.2012.08.005

    Article  Google Scholar 

Download references

Acknowledgements

Data acquisition was made possible by Riccarda Lomuscio, Dr. Marina Mancini, Rita Masellis, Dr. Annamaria Porcelli, Dr. Tiziana Quarto, Dr. Raffaella Romano, and Dr. Leonardo Fazio (Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro).

Funding

This work was supported by a “Capitale Umano ad Alta Qualificazione” Grant by Fondazione Con Il Sud and by the “Ricerca Finalizzata” Grant (number: PE-2011-02347951) awarded to Alessandro Bertolino, as well as by a Hoffmann-La Roche Collaboration Grant awarded to Giulio Pergola. This project has received funding from the European Union Seventh Framework Programme for research, technological development and demonstration under Grant agreement no. 602450. This paper reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. The funding bodies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Giuseppe Blasi.

Ethics declarations

Conflict of interest

Alessandro Bertolino is a stockholder of Hoffmann-La Roche Ltd. He has also received lecture fees from Otsuka, Jannsen, Lundbeck, and consultant fees from Biogen. Giulio Pergola has been the academic supervisor of a Hoffmann-La Roche collaboration Grant (years 2015–2016) that funds his and Antonio Rampino’s salary. Antonio Rampino has received travel fees from Lundbeck. All other authors declare no biomedical financial interests and no potential conflicts of interest.

Ethical statement

All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 520 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Antonucci, L.A., Di Carlo, P., Passiatore, R. et al. Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression. Brain Struct Funct 224, 1331–1344 (2019). https://doi.org/10.1007/s00429-019-01843-7

Download citation

Keywords

  • Coordinated gene expression
  • Independent Component Analysis
  • Medio-dorsal nucleus
  • DLPFC
  • fMRI
  • Schizophrenia