Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-21T17:07:09.823Z Has data issue: false hasContentIssue false
  • English
  • Français

MIR137 polygenic risk is associated with schizophrenia and affects functional connectivity of the dorsolateral prefrontal cortex

Published online by Cambridge University Press:  26 June 2019

Shu Liu ,
Ang Li ,
Yong Liu ,
Jin Li ,
Meng Wang ,
Yuqing Sun ,
Wen Qin ,
Chunshui Yu ,
Tianzi Jiang  and
Bing Liu Open the ORCID record for Bing Liu [Opens in a new window]
Shu Liu
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China
Ang Li
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China
Yong Liu
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China
Jin Li
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China
Meng Wang
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China
Yuqing Sun
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China
Wen Qin
Affiliation:
Department of Radiology, Tianjin Medical University General Hospital, Tianjin300052, China
Chunshui Yu
Affiliation:
Department of Radiology, Tianjin Medical University General Hospital, Tianjin300052, China
Tianzi Jiang
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu610054, China Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
Bing Liu*
Affiliation:
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China University of Chinese Academy of Sciences, Beijing100049, China CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing100190, China
*
Author for correspondence: Bing Liu, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Genome-wide association studies (GWAS) have consistently revealed that a variant of microRNA 137 (MIR137) shows a quite significant association with schizophrenia. Identifying the network of genes regulated by MIR137 could provide insights into the biological processes underlying schizophrenia. In addition, DLPFC functional connectivity, a robust correlate of MIR137, may provide plausible endophenotypes. However, the regulatory role of the MIR137 gene network in the disrupted functional connectivity remains unclear. Here, we tested the effects of the MIR137 regulated genes on the risk for schizophrenia and DLPFC functional connectivity.

Methods

To evaluate the additive effects of the MIR137 regulated genes (N = 1274), we calculated a MIR137 polygenic risk score (PRS) for schizophrenia and tested its association with the risk for schizophrenia in the genomic data of a Han Chinese population that included schizophrenia patients (N = 589) and normal controls (N = 575). We then investigated the association between MIR137 PRS and DLPFC functional connectivity in two independent young healthy cohorts (N = 356 and N = 314).

Results

We found that the MIR137 PRS successfully captured the differences in genetic structure between the patients and controls, but the single gene MIR137 did not. We then consistently found that a higher MIR137 PRS was correlated with lower functional connectivities between the DLPFC and both the superior parietal cortex and the inferior temporal cortex in two independent cohorts.

Conclusion

The findings suggested that these two functional connectivities of the DLPFC could be important endophenotypes linking the MIR137-regulated genetic structure to schizophrenia.

Keywords

Dorsolateral prefrontal cortexfunctional connectivitymicroRNA-137polygenic risk scoreschizophrenia
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 9 , July 2020 , pp. 1510 - 1518
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aertsen, AM, Gerstein, GL, Habib, MK and Palm, G (1989) Dynamics of neuronal firing correlation: modulation of ‘effective connectivity’. Journal of Neurophysiology 61, 900–917.CrossRefGoogle Scholar
Baker, JT, Holmes, AJ, Masters, GA, Yeo, BTT, Krienen, F, Buckner, RL and Ongür, D (2014) Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. JAMA Psychiatry 71, 109–118.CrossRefGoogle ScholarPubMed
Barch, DM, Moore, H, Nee, DE, Manoach, DS and Luck, SJ (2012) CNTRICS imaging biomarkers selection: working memory. Schizophrenia Bulletin 38, 43–52.CrossRefGoogle ScholarPubMed
Bjorkquist, OA, Olsen, EK, Nelson, BD and Herbener, ES (2016) Altered amygdala-prefrontal connectivity during emotion perception in schizophrenia. Schizophrenia Research 175, 35–41.CrossRefGoogle Scholar
Cosgrove, D, Harold, D, Mothersill, O, Anney, R, Hill, MJ, Bray, NJ, Blokland, G, Petryshen, T, Richards, A, Mantripragada, K, Owen, M, O'Donovan, MC, Gill, M, Corvin, A, Morris, DW and Donohoe, G (2017) MiR-137-derived polygenic risk: effects on cognitive performance in patients with schizophrenia and controls. Translational Psychiatry 7, e1012.CrossRefGoogle ScholarPubMed
Cosgrove, D, Mothersill, DO, Whitton, L, Harold, D, Kelly, S, Holleran, L, Holland, J, Anney, R, Richards, A, Mantripragada, K, Owen, M, O'Donovan, MC, Gill, M, Corvin, A, Morris, DW and Donohoe, G (2018) Effects of MiR-137 genetic risk score on brain volume and cortical measures in patients with schizophrenia and controls. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics 177, 369–376.CrossRefGoogle ScholarPubMed
D'Esposito, M, Postle, BR and Rypma, B (2000) Prefrontal cortical contributions to working memory: evidence from event-related fMRI studies. Experimental Brain Research 133, 3–11.CrossRefGoogle ScholarPubMed
Delaneau, O, Marchini, J and Zagury, JF (2012) A linear complexity phasing method for thousands of genomes. Nature Methods 9, 179–181.CrossRefGoogle Scholar
Fellrath, J, Mottaz, A, Schnider, A, Guggisberg, AG and Ptak, R (2016) Theta-band functional connectivity in the dorsal fronto-parietal network predicts goal-directed attention. Neuropsychologia 92, 20–30.CrossRefGoogle ScholarPubMed
Filipowicz, W, Bhattacharyya, SN and Sonenberg, N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nature Reviews Genetics 9, 102–114.CrossRefGoogle ScholarPubMed
Guan, F, Zhang, B, Yan, T, Li, L, Liu, F, Li, T, Feng, Z, Zhang, B, Liu, X and Li, S (2014) MIR137 gene and target gene CACNA1C of miR-137 contribute to schizophrenia susceptibility in Han Chinese. Schizophrenia Research 152, 97–104.CrossRefGoogle ScholarPubMed
Guella, I, Sequeira, A, Rollins, B, Morgan, L, Torri, F, van Erp, TGM, Myers, RM, Barchas, JD, Schatzberg, AF, Watson, SJ, Akil, H, Bunney, WE, Potkin, SG, Macciardi, F and Vawter, MP (2013) Analysis of miR-137 expression and rs1625579 in dorsolateral prefrontal cortex. Journal of Psychiatric Research 47, 1215–1221.CrossRefGoogle ScholarPubMed
Hass, J, Walton, E, Kirsten, H, Turner, J, Wolthusen, R, Roessner, V, Sponheim, SR, Holt, D, Gollub, R, Calhoun, VD and Ehrlich, S (2015) Complexin2 modulates working memory-related neural activity in patients with schizophrenia. European Archives of Psychiatry and Clinical Neuroscience 265, 137–145.CrossRefGoogle ScholarPubMed
Hill, MJ, Donocik, JG, Nuamah, RA, Mein, CA, Sainz-Fuertes, R and Bray, NJ (2014) Transcriptional consequences of schizophrenia candidate miR-137 manipulation in human neural progenitor cells. Schizophrenia Research 153, 225–230.CrossRefGoogle ScholarPubMed
Hong, EP, Park, JW and Size, S (2012) Sample size and statistical power calculation in genetic association studies. Genomics & Informatics 10, 117–122.CrossRefGoogle ScholarPubMed
Howie, BN, Donnelly, P and Marchini, J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics 5, e1000529.CrossRefGoogle ScholarPubMed
Kang, SS, Sponheim, SR, Chafee, MV and MacDonald, AW (2011) Disrupted functional connectivity for controlled visual processing as a basis for impaired spatial working memory in schizophrenia. Neuropsychologia 49, 2836–2847.CrossRefGoogle Scholar
Li, Y, Wang, E, Zhang, H, Dou, S, Liu, L, Tong, L, Lei, Y, Wang, M, Xu, J, Shi, D and Zhang, Q (2014) Functional connectivity changes between parietal and prefrontal cortices in primary insomnia patients: Evidence from resting-state fMRI. European Journal of Medical Research 19, 32.CrossRefGoogle ScholarPubMed
Li, Z, Chen, J, Yu, H, He, L, Xu, Y, Zhang, D, Yi, Q, Li, C, Li, X, Shen, J, Song, Z, Ji, W, Wang, M, Zhou, J, Chen, B, Liu, Y, Wang, J, Wang, P, Yang, P, Wang, Q, Feng, G, Liu, B, Sun, W, Li, B, He, G, Li, W, Wan, C, Xu, Q, Li, W, Wen, Z, Liu, K, Huang, F, Ji, J, Ripke, S, Yue, W, Sullivan, PF, O'Donovan, MC and Shi, Y (2017) Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nature Genetics 49, 1576–1583.CrossRefGoogle Scholar
Liu, B, Zhang, X, Hou, B, Li, J, Qiu, C, Qin, W, Yu, C and Jiang, T (2014) The impact of MIR137 on dorsolateral prefrontal-hippocampal functional connectivity in healthy subjects. Neuropsychopharmacology 39, 2153–2160.CrossRefGoogle ScholarPubMed
Ma, G, Yin, J, Fu, J, Luo, X, Zhou, H, Tao, H, Cui, L, Li, Y, Lin, Z, Zhao, B, Li, Z, Lin, J and Li, K (2014) Association of a miRNA-137 polymorphism with schizophrenia in a Southern Chinese Han Population. BioMed Research International 2014, 751267.CrossRefGoogle Scholar
Mahmoudi, E and Cairns, MJ (2017) MiR-137: an important player in neural development and neoplastic transformation. Molecular Psychiatry 22, 44–55.CrossRefGoogle ScholarPubMed
Mothersill, O, Morris, DW, Kelly, S, Rose, EJ, Fahey, C, O'Brien, C, Lyne, R, Reilly, R, Gill, M, Corvin, AP and Donohoe, G (2014) Effects of MIR137 on fronto-amygdala functional connectivity. NeuroImage 90, 189–195.CrossRefGoogle ScholarPubMed
Neufang, S, Geiger, MJ, Homola, GA, Mahr, M, Akhrif, A, Nowak, J, Reif, A, Romanos, M, Deckert, J, Solymosi, L and Domschke, K (2015) Modulation of prefrontal functioning in attention systems by NPSR1 gene variation. NeuroImage 114, 199–206.CrossRefGoogle ScholarPubMed
Park, J, Chun, JW, Park, HJ, Kim, E and Kim, JJ (2018) Involvement of amygdala–prefrontal dysfunction in the influence of negative emotion on the resolution of cognitive conflict in patients with schizophrenia. Brain and Behavior 8, e01064.CrossRefGoogle ScholarPubMed
Patterson, N, Price, AL and Reich, D (2006) Population structure and eigenanalysis. PLoS Genetics 2, 2074–2093.CrossRefGoogle ScholarPubMed
Poppe, AB, Barch, DM, Carter, CS, Gold, JM, Ragland, JD, Silverstein, SM and MacDonald, AW (2016) Reduced frontoparietal activity in schizophrenia is linked to a specific deficit in goal maintenance: a multisite functional imaging study. Schizophrenia Bulletin 42, 1149–1157.CrossRefGoogle ScholarPubMed
Potkin, SG, Turner, JA, Brown, GG, McCarthy, G, Greve, DN, Glover, GH, Manoach, DS, Belger, A, Diaz, M, Wible, CG, Ford, JM, Mathalon, DH, Gollub, R, Lauriello, J, O'Leary, D, Van Erp, TGM, Toga, AW, Preda, A and Lim, KO (2009) Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study. Schizophrenia Bulletin 35, 19–31.CrossRefGoogle ScholarPubMed
Potkin, SG, Macciardi, F, Guffanti, G, Fallon, JH, Wang, Q, Turner, JA, Lakatos, A, Miles, MF, Lander, A, Vawter, MP and Xie, X (2010) Identifying gene regulatory networks in schizophrenia. NeuroImage 53, 839–847.CrossRefGoogle Scholar
Potkin, SG, Van Erp, TGM, Guella, I, Vawter, MP, Turner, J, Brown, GG, McCarthy, G, Greve, DN, Glover, GH, Calhoun, VD, Lim, KO, Bustillo, JR, Belger, A, Ford, JM, Mathalon, DH, Diaz, M, Preda, A, Nguyen, D and Macciardi, F (2014) Schizophrenia miR-137 locus risk genotype is associated with dorsolateral prefrontal cortex hyperactivation. Biological Psychiatry 75, 398–405.Google Scholar
Price, AL, Patterson, NJ, Plenge, RM, Weinblatt, ME, Shadick, NA and Reich, D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38, 904–909.CrossRefGoogle ScholarPubMed
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MAR, Bender, D, Maller, J, Sklar, P, de Bakker, PIW, Daly, MJ and Sham, PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559–575.CrossRefGoogle ScholarPubMed
Ripke, S, O'Dushlaine, C, Chambert, K, Moran, JL, Kähler, AK, Akterin, S, Bergen, S, Collins, AL, Crowley, JJ, Fromer, M, Kim, Y, Lee, SH, Magnusson, PK, Sanchez, N, Stahl, EA, Williams, S, Wray, NR, Xia, K, Bettella, F, Børglum, AD, Bulik-Sullivan, BK, Cormican, P, Craddock, N, de Leeuw, C, Durmishi, N, Gill, M, Golimbet, V, Hamshere, ML, Holmans, P, Hougaard, DM, Kendler, KS, Lin, K, Morris, DW, Mors, O, Mortensen, PB, Neale, BM, O'Neill, FA, Owen, MJ, Milovancevic, M, Posthuma, D, Powell, J, Richards, AL, Riley, BP, Ruderfer, D, Rujescu, D, Sigurdsson, E, Silagadze, T, Smit, AB, Stefansson, H, Steinberg, S, Suvisaari, J, Tosato, S, Verhage, M, Walters, JT, Bramon, E, Corvin, AP, O'Donovan, MC, Stefansson, K, Scolnick, E, Purcell, S, McCarroll, S, Sklar, P, Hultman, CM and Sullivan, PF (2013) Genome-wide association analysis identifies 14 new risk loci for schizophrenia. Nature Genetics 45, 1150–1159.CrossRefGoogle Scholar
Ripke, S, Neale, BM, Corvin, A, Walters, JTR, Farh, KH, Holmans, PA, Lee, P, Bulik-Sullivan, B, Collier, DA, Huang, H, Pers, TH, Agartz, I, Agerbo, E, Albus, M, Alexander, M, Amin, F, Bacanu, SA, Begemann, M, Belliveau, RA, Bene, J, Bergen, SE, Bevilacqua, E, Bigdeli, TB, Black, DW, Bruggeman, R, Buccola, NG, Buckner, RL, Byerley, W, Cahn, W, Cai, G, Campion, D, Cantor, RM, Carr, VJ, Carrera, N, Catts, SV, Chambert, KD, Chan, RCK, Chen, RYL, Chen, EYH, Cheng, W, Cheung, EFC, Chong, SA, Cloninger, CR, Cohen, D, Cohen, N, Cormican, P, Craddock, N, Crowley, JJ, Curtis, D, Davidson, M, Davis, KL, Degenhardt, F, Del Favero, J, Demontis, D, Dikeos, D, Dinan, T, Djurovic, S, Donohoe, G, Drapeau, E, Duan, J, Dudbridge, F, Durmishi, N, Eichhammer, P, Eriksson, J, Escott-Price, V, Essioux, L, Fanous, AH, Farrell, MS, Frank, J, Franke, L, Freedman, R, Freimer, NB, Friedl, M, Friedman, JI, Fromer, M, Genovese, G, Georgieva, L, Giegling, I, Giusti-Rodríguez, P, Godard, S, Goldstein, JI, Golimbet, V, Gopal, S, Gratten, J, De Haan, L, Hammer, C, Hamshere, ML, Hansen, M, Hansen, T, Haroutunian, V, Hartmann, AM, Henskens, FA, Herms, S, Hirschhorn, JN, Hoffmann, P, Hofman, A, Hollegaard, MV, Hougaard, DM, Ikeda, M, Joa, I, Julià, A, Kahn, RS, Kalaydjieva, L, Karachanak-Yankova, S, Karjalainen, J, Kavanagh, D, Keller, MC, Kennedy, JL, Khrunin, A, Kim, Y, Klovins, J, Knowles, JA, Konte, B, Kucinskas, V, Kucinskiene, ZA, Kuzelova-Ptackova, H, Kähler, AK, Laurent, C, Keong, JLC, Lee, SH, Legge, SE, Lerer, B, Li, M, Li, T, Liang, KY, Lieberman, J, Limborska, S, Loughland, CM, Lubinski, J, Lönnqvist, J, Macek, M, Magnusson, PKE, Maher, BS, Maier, W, Mallet, J, Marsal, S, Mattheisen, M, Mattingsdal, M, McCarley, RW, McDonald, C, McIntosh, AM, Meier, S, Meijer, CJ, Melegh, B, Melle, I, Mesholam-Gately, RI, Metspalu, A, Michie, PT, Milani, L, Milanova, V, Mokrab, Y, Morris, DW, Mors, O, Murphy, KC, Murray, RM, Myin-Germeys, I, Müller-Myhsok, B, Nelis, M, Nenadic, I, Nertney, DA, Nestadt, G, Nicodemus, KK, Nikitina-Zake, L, Nisenbaum, L, Nordin, A, Callaghan, EO, Dushlaine, CO, Neill, FAO, Oh, SY, Olincy, A, Olsen, L, Os, JV, Pantelis, C, Papadimitriou, GN, Papiol, S, Parkhomenko, E, Pato, MT, Paunio, T, Pejovic-Milovancevic, M, Perkins, DO, Pietiläinen, O, Pimm, J, Pocklington, AJ, Powell, J, Price, A, Pulver, AE, Purcell, SM, Quested, D, Rasmussen, HB, Reichenberg, A, Reimers, MA, Richards, AL, Roffman, JL, Roussos, P, Ruderfer, DM, Salomaa, V, Sanders, AR, Schall, U, Schubert, CR, Schulze, TG, Schwab, SG, Scolnick, EM, Scott, RJ, Seidman, LJ, Shi, J, Sigurdsson, E, Silagadze, T, Silverman, JM, Sim, K, Slominsky, P, Smoller, JW, So, HC, Spencer, CA, Stahl, EA, Stefansson, H, Steinberg, S, Stogmann, E, Straub, RE, Strengman, E, Strohmaier, J, Stroup, TS, Subramaniam, M, Suvisaari, J, Svrakic, DM, Szatkiewicz, JP, Söderman, E, Thirumalai, S, Toncheva, D, Tosato, S, Veijola, J, Waddington, J, Walsh, D, Wang, D, Wang, Q, Webb, BT, Weiser, M, Wildenauer, DB, Williams, NM, Williams, S, Witt, SH, Wolen, AR, Wong, EHM, Wormley, BK, Xi, HS, Zai, CC, Zheng, X, Zimprich, F, Wray, NR, Stefansson, K, Visscher, PM, Wellcome Trust Case-Control Consortium, Adolfsson, R, Andreassen, OA, Blackwood, DHR, Bramon, E, Buxbaum, JD, Børglum, AD, Cichon, S, Darvasi, A, Domenici, E, Ehrenreich, H, Esko, T, Gejman, PV, Gill, M, Gurling, H, Hultman, CM, Iwata, N, Jablensky, AV, Jönsson, EG, Kendler, KS, Kirov, G, Knight, J, Lencz, T, Levinson, DF, Li, QS, Liu, J, Malhotra, AK, McCarroll, SA, McQuillin, A, Moran, JL, Mortensen, PB, Mowry, BJ, Nöthen, MM, Ophoff, RA, Owen, MJ, Palotie, A, Pato, CN, Petryshen, TL, Posthuma, D, Rietschel, M, Riley, BP, Rujescu, D, Sham, PC, Sklar, P, Clair, DS, Weinberger, DR, Wendland, JR, Werge, T, Daly, MJ, Sullivan, PF and Donovan, MCO (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427.Google Scholar
Rottschy, C, Langner, R, Dogan, I, Reetz, K, Laird, AR, Schulz, JB, Fox, PT and Eickhoff, SB (2012) Modelling neural correlates of working memory: a coordinate-based meta-analysis. NeuroImage 60, 830–846.CrossRefGoogle ScholarPubMed
Schizophrenia Psychiatric Genome-Wide Association Study C (2011) Genome-wide association study identifies five new schizophrenia loci. Nature Genetics 43, 969–976.CrossRefGoogle Scholar
Sheffield, JM, Repovs, G, Harms, MP, Carter, CS, Gold, JM, MacDonald, AW, Daniel Ragland, J, Silverstein, SM, Godwin, D and Barch, DM (2015) Fronto-parietal and cingulo-opercular network integrity and cognition in health and schizophrenia. Neuropsychologia 73, 82–93.CrossRefGoogle Scholar
Shirer, WR, Jiang, H, Price, CM, Ng, B and Greicius, MD (2015) Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination. NeuroImage 117, 67–79.CrossRefGoogle ScholarPubMed
Siegert, S, Seo, J, Kwon, EJ, Rudenko, A, Cho, S, Wang, W, Flood, Z, Martorell, AJ, Ericsson, M, Mungenast, AE and Tsai, LH (2015) The schizophrenia risk gene product miR-137 alters presynaptic plasticity. Nature Neuroscience 18, 1008–1016.CrossRefGoogle ScholarPubMed
Smrt, RD, Szulwach, KE, Pfeiffer, RL, Li, X, Guo, W, Pathania, M, Teng, ZQ, Luo, Y, Peng, J, Bordey, A, Jin, P and Zhao, X (2010) MicroRNA miR-137 regulates neuronal maturation by targeting ubiquitin ligase mind bomb-1. Stem Cells (Dayton, Ohio) 28, 1060–1070.CrossRefGoogle ScholarPubMed
Spreng, RN, Sepulcre, J, Turner, GR, Stevens, WD and Schacter, DL (2013) Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. Journal of Cognitive Neuroscience 25, 74–86.CrossRefGoogle ScholarPubMed
Su, T-W, Lan, T-H, Hsu, T-W, Biswal, BB, Tsai, P-J, Lin, W-C and Lin, C-P (2013) Reduced neuro-integration from the dorsolateral prefrontal cortex to the whole brain and executive dysfunction in schizophrenia patients and their relatives. Schizophrenia Research 148, 50–58.CrossRefGoogle ScholarPubMed
Sun, G, Ye, P, Murai, K, Lang, MF, Li, S, Zhang, H, Li, W, Fu, C, Yin, J, Wang, A, Ma, X and Shi, Y (2011) MiR-137 forms a regulatory loop with nuclear receptor TLX and LSD1 in neural stem cells. Nature Communications 2, 529.CrossRefGoogle ScholarPubMed
Sun, YJ, Yu, Y, Zhu, GC, Sun, ZH, Xu, J, Cao, JH and Ge, JX (2015) Association between single nucleotide polymorphisms in MiR219-1 and MiR137 and susceptibility to schizophrenia in a Chinese population. FEBS Open Bio 5, 774–778.CrossRefGoogle ScholarPubMed
Szulwach, KE, Li, X, Smrt, RD, Li, Y, Luo, Y, Lin, L, Santistevan, NJ, Li, W, Zhao, X and Jin, P (2010) Cross talk between microRNA and epigenetic regulation in adult neurogenesis. Journal of Cell Biology 189, 127–141.CrossRefGoogle ScholarPubMed
Thorisson, GA, Smith, AV, Krishnan, L and Stein, LD (2005) The international HapMap project Web site. Genome Research 15, 1592–1593.CrossRefGoogle ScholarPubMed
Vallès, A, Martens, GJM, De Weerd, P, Poelmans, G and Aschrafi, A (2014) MicroRNA-137 regulates a glucocorticoid receptor-dependent signalling network: implications for the etiology of schizophrenia. Journal of Psychiatry and Neuroscience 39, 312–320.CrossRefGoogle ScholarPubMed
Vincent, JL, Kahn, I, Snyder, AZ, Raichle, ME and Buckner, RL (2008) Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology 100, 3328–3342.CrossRefGoogle ScholarPubMed
Wendelken, C, Bunge, SA and Carter, CS (2008) Maintaining structured information: an investigation into functions of parietal and lateral prefrontal cortices. Neuropsychologia 46, 665–678.CrossRefGoogle ScholarPubMed
Williams, HJ, Norton, N, Dwyer, S, Moskvina, V, Nikolov, I, Carroll, L, Georgieva, L, Williams, NM, Morris, DW, Quinn, EM, Giegling, I, Ikeda, M, Wood, J, Lencz, T, Hultman, C, Lichtenstein, P, Thiselton, D, Maher, BS, Malhotra, AK, Riley, B, Kendler, KS, Gill, M, Sullivan, P, Sklar, P, Purcell, S, Nimgaonkar, VL, Kirov, G, Holmans, P, Corvin, A, Rujescu, D, Craddock, N, Owen, MJ and O'Donovan, MC (2011) Fine mapping of ZNF804A and genome-wide significant evidence for its involvement in schizophrenia and bipolar disorder. Molecular Psychiatry 16, 429–441.CrossRefGoogle ScholarPubMed
Williams-Gray, CH, Hampshire, A, Robbins, TW, Owen, AM and Barker, RA (2007) Catechol O-methyltransferase val158met genotype influences frontoparietal activity during planning in patients with parkinson's disease. Journal of Neuroscience 27, 4832–4838.CrossRefGoogle ScholarPubMed
Woloszyn, L and Sheinberg, DL (2009) Neural dynamics in inferior temporal cortex during a visual working memory task. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 29, 5494–5507.CrossRefGoogle ScholarPubMed
Wright, C, Turner, JA, Calhoun, VD and Perrone-Bizzozero, N (2013) Potential impact of miR-137 and its targets in schizophrenia. Frontiers in Genetics 4, 58.CrossRefGoogle Scholar
Yuan, J, Cheng, Z, Zhang, F, Zhou, Z, Yu, S and Jin, C (2014) Lack of association between microRNA-137 SNP rs1625579 and schizophrenia in a replication study of Han Chinese. Molecular Genetics and Genomics 290, 297–301.CrossRefGoogle Scholar
Yue, JL, Li, P, Shi, L, Lin, X, Sun, HQ and Lu, L (2018) Enhanced temporal variability of amygdala-frontal functional connectivity in patients with schizophrenia. NeuroImage: Clinical 18, 527–532.CrossRefGoogle ScholarPubMed
Zhang, P, Bian, Y, Liu, N, Tang, Y, Pan, C, Hu, Y and Tang, Z (2016) The SNP rs1625579 in MIR-137 gene and risk of schizophrenia in Chinese population: a meta-analysis. Comprehensive Psychiatry 67, 26–32.CrossRefGoogle ScholarPubMed
Zhang, Z, Yan, T, Wang, Y, Zhang, Q, Zhao, W, Chen, X, Zhai, J, Chen, M, Du, B, Deng, X, Ji, F, Xiang, Y, Wu, H, Song, J, Dong, Q, Chen, C and Li, J (2018) Polymorphism in schizophrenia risk gene MIR137 is associated with the posterior cingulate Cortex's activation and functional and structural connectivity in healthy controls. NeuroImage: Clinical 19, 160–166.CrossRefGoogle ScholarPubMed
Supplementary material: File

Liu et al. supplementary material

Liu et al. supplementary material 1

Download Liu et al. supplementary material(File)
File 211.4 KB
Supplementary material: File

Liu et al. supplementary material

Liu et al. supplementary material 2

Download Liu et al. supplementary material(File)
File 39.8 KB
Supplementary material: File

Liu et al. supplementary material

Liu et al. supplementary material 3

Download Liu et al. supplementary material(File)
File 654.8 KB