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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Developing a Genetic Biomarker-based Diagnostic Model for Major Depressive Disorder using Random Forests and Artificial Neural Networks

Author(s): Wei Gu, Tinghong Ming and Zhongwen Xie*

Volume 26, Issue 2, 2023

Published on: 02 June, 2022

Page: [424 - 435] Pages: 12

DOI: 10.2174/1386207325666220404123433

Price: $65

Abstract

Background: The clinical diagnosis of major depressive disorder (MDD) mainly relies on subjective assessment of depression-like behaviors and clinical examination. In the present study, we aimed to develop a novel diagnostic model for specially predicting MDD.

Methods: The human brain GSE102556 DataSet and the blood GSE98793 and GSE76826 Data Sets were downloaded from the Gene Expression Omnibus (GEO) database. We used a novel algorithm, random forest (RF) plus artificial neural network (ANN), to examine gene biomarkers and establish a diagnostic model of MDD.

Results: Through the “limma” package in the R language, 2653 differentially expressed genes (DEGs) were identified in the GSE102556 DataSet, and 1786 DEGs were identified in the GSE98793 DataSet, and a total of 100 shared DEGs. We applied GSE98793 TrainData 1 to an RF algorithm and thereby successfully selected 28 genes as biomarkers. Furthermore, 28 biomarkers were verified by GSE98793 TestData 1, and the performance of these biomarkers was found to be perfect. In addition, we further used an ANN algorithm to optimize the weight of each gene and employed GSE98793 TrainData 2 to build an ANN model through the neural net package by R language. Based on this algorithm, GSE98793 TestData 2 and independent blood GSE76826 were verified to correlate with MDD, with AUCs of 0.903 and 0.917, respectively.

Conclusion: To the best of our knowledge, this is the first time that the classifier constructed via DEG biomarkers has been used as an endophenotype for MDD clinical diagnosis. Our results may provide a new entry point for the diagnosis, treatment, outcome prediction, prognosis and recurrence of MDD.

Keywords: Major depressive disorder, biomarkers, genome-wide microarray analysis, ensemble learning, gene expression profiling, neuropsychiatric disorder.

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[1]
Thwala, J.D.; Sherwood, P.M.; Edwards, S.D. Description of philophonetics counselling as expressive therapeutic modality for treating depression. AI Soc., 2019, 34(3), 609-614.
[http://dx.doi.org/10.1007/s00146-018-0805-0]
[2]
Fässberg, M.M.; Cheung, G.; Canetto, S.S.; Erlangsen, A.; Lapierre, S.; Lindner, R.; Draper, B.; Gallo, J.J.; Wong, C.; Wu, J.; Duberstein, P.; Wærn, M. A systematic review of physical illness, functional disability, and suicidal behaviour among older adults. Aging Ment. Health, 2016, 20(2), 166-194.
[http://dx.doi.org/10.1080/13607863.2015.1083945] [PMID: 26381843]
[3]
Li, Y.F. A hypothesis of monoamine (5-HT) - Glutamate/GABA long neural circuit: Aiming for fast-onset antidepressant discovery. Pharmacol. Ther., 2020, 208, 107494.
[http://dx.doi.org/10.1016/j.pharmthera.2020.107494] [PMID: 31991195]
[4]
Aronson, J.K.; Ferner, R.E. Biomarkers-a general review. Curr. Protoc. Pharmacol, 201776, 9.23.1-9.23.17. http://dx.doi.org/10.1002/cpph.19.
[5]
Lakhan, S.E.; Vieira, K.; Hamlat, E. Biomarkers in psychiatry: Drawbacks and potential for misuse. Int. Arch. Med., 2010, 3, 1.
[http://dx.doi.org/10.1186/1755-7682-3-1] [PMID: 20150988]
[6]
Gururajan, A.; Clarke, G.; Dinan, T.G.; Cryan, J.F. Molecular biomarkers of depression. Neurosci. Biobehav. Rev., 2016, 64, 101-133.
[http://dx.doi.org/10.1016/j.neubiorev.2016.02.011] [PMID: 26906761]
[7]
Schneider, B.; Prvulovic, D. Novel biomarkers in major depression. Curr. Opin. Psychiatry, 2013, 26(1), 47-53.
[http://dx.doi.org/10.1097/YCO.0b013e32835a5947] [PMID: 23154643]
[8]
Menke, A. Gene expression: Biomarker of antidepressant therapy? Int. Rev. Psychiatry, 2013, 25(5), 579-591.
[http://dx.doi.org/10.3109/09540261.2013.825580] [PMID: 24151803]
[9]
Papakostas, G.I. Surrogate markers of treatment outcome in major depressive disorder. Int. J. Neuropsychopharmacol., 2012, 15(6), 841-854.
[http://dx.doi.org/10.1017/S1461145711001246] [PMID: 21813045]
[10]
Chen, C.Y.; Yeh, Y.W.; Kuo, S.C.; Liang, C.S.; Ho, P.S.; Huang, C.C.; Yen, C.H.; Shyu, J.F.; Lu, R.B.; Huang, S.Y. Differences in immunomodulatory properties between venlafaxine and paroxetine in patients with major depressive disorder. Psychoneuroendocrinology, 2018, 87, 108-118.
[http://dx.doi.org/10.1016/j.psyneuen.2017.10.009] [PMID: 29055264]
[11]
Turck, C.W.; Guest, P.C.; Maccarrone, G.; Ising, M.; Kloiber, S.; Lucae, S.; Holsboer, F.; Martins-de-Souza, D. Proteomic differences in blood plasma associated with antidepressant treatment response. Front. Mol. Neurosci., 2017, 10, 272.
[http://dx.doi.org/10.3389/fnmol.2017.00272] [PMID: 28912679]
[12]
Murck, H.; Braunisch, M.C.; Konrad, C.; Jezova, D.; Kircher, T. Markers of mineralocorticoid receptor function: Changes over time and relationship to response in patients with major depression. Int. Clin. Psychopharmacol., 2019, 34(1), 18-26.
[http://dx.doi.org/10.1097/YIC.0000000000000239] [PMID: 30300165]
[13]
Kunugi, H.; Hori, H.; Ogawa, S. Biochemical markers subtyping major depressive disorder. Psychiatry Clin. Neurosci., 2015, 69(10), 597-608.
[http://dx.doi.org/10.1111/pcn.12299] [PMID: 25825158]
[14]
Spijker, S.; Van Zanten, J.S.; De Jong, S.; Penninx, B.W.; van Dyck, R.; Zitman, F.G.; Smit, J.H.; Ylstra, B.; Smit, A.B.; Hoogendijk, W.J. Stimulated gene expression profiles as a blood marker of major depressive disorder. Biol. Psychiatry, 2010, 68(2), 179-186.
[http://dx.doi.org/10.1016/j.biopsych.2010.03.017] [PMID: 20471630]
[15]
Papakostas, G.I.; Shelton, R.C.; Kinrys, G.; Henry, M.E.; Bakow, B.R.; Lipkin, S.H.; Pi, B.; Thurmond, L.; Bilello, J.A. Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: A pilot and replication study. Mol. Psychiatry, 2013, 18(3), 332-339.
[http://dx.doi.org/10.1038/mp.2011.166] [PMID: 22158016]
[16]
Singh, A.V.; Chandrasekar, V.; Janapareddy, P.; Mathews, D.E.; Laux, P.; Luch, A.; Yang, Y.; Garcia-Canibano, B.; Balakrishnan, S.; Abinahed, J.; Al Ansari, A.; Dakua, S.P. Emerging application of nanorobotics and artificial intelligence to cross the BBB: Advances in design, controlled maneuvering, and targeting of the barriers. ACS Chem. Neurosci., 2021, 12(11), 1835-1853.
[http://dx.doi.org/10.1021/acschemneuro.1c00087] [PMID: 34008957]
[17]
Singh, A.V.; Ansari, M.H.D.; Rosenkranz, D.; Maharjan, R.S.; Kriegel, F.L.; Gandhi, K.; Kanase, A.; Singh, R.; Laux, P.; Luch, A. Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine. Adv. Healthc. Mater., 2020, 9(17), e1901862.
[http://dx.doi.org/10.1002/adhm.201901862] [PMID: 32627972]
[18]
Strobl, C.; Boulesteix, A.L.; Zeileis, A.; Hothorn, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 2007, 8, 25.
[http://dx.doi.org/10.1186/1471-2105-8-25] [PMID: 17254353]
[19]
Lin, E.; Hwang, Y.; Wang, S.C.; Gu, Z.J.; Chen, E.Y. An artificial neural network approach to the drug efficacy of interferon treatments. Pharmacogenomics, 2006, 7(7), 1017-1024.
[http://dx.doi.org/10.2217/14622416.7.7.1017] [PMID: 17054412]
[20]
Kong, Y.; Yu, T. A deep neural network model using random forest to extract feature representation for gene expression data classification. Sci. Rep., 2018, 8(1), 16477.
[http://dx.doi.org/10.1038/s41598-018-34833-6] [PMID: 30405137]
[21]
Sullivan, P.F.; Fan, C.; Perou, C.M. Evaluating the comparability of gene expression in blood and brain. Am. J. Med. Genet. B. Neuropsychiatr. Genet., 2006, 141B(3), 261-268.
[http://dx.doi.org/10.1002/ajmg.b.30272] [PMID: 16526044]
[22]
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7), e47.
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[23]
Zhong, X.; Liu, Y.; Liu, H.; Zhang, Y.; Wang, L.; Zhang, H. Identification of potential prognostic genes for neuroblastoma. Front. Genet., 2018, 9, 589.
[http://dx.doi.org/10.3389/fgene.2018.00589] [PMID: 30555514]
[24]
Chen, L.; Zhang, Y.H.; Wang, S.; Zhang, Y.; Huang, T.; Cai, Y.D. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS One, 2017, 12(9), e0184129.
[http://dx.doi.org/10.1371/journal.pone.0184129] [PMID: 28873455]
[25]
Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45(D1), D353-D361.
[http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]
[26]
Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; Harris, M.A.; Hill, D.P.; Issel-Tarver, L.; Kasarskis, A.; Lewis, S.; Matese, J.C.; Richardson, J.E.; Ringwald, M.; Rubin, G.M.; Sherlock, G. Gene ontology: Tool for the unification of biology. Nat. Genet., 2000, 25(1), 25-29.
[http://dx.doi.org/10.1038/75556] [PMID: 10802651]
[27]
Dennis, G., Jr; Sherman, B.T.; Hosack, D.A.; Yang, J.; Gao, W.; Lane, H.C.; Lempicki, R.A. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol., 2003, 4(5), 3.
[http://dx.doi.org/10.1186/gb-2003-4-5-p3] [PMID: 12734009]
[28]
Jentsch, M.C.; Van Buel, E.M.; Bosker, F.J.; Gladkevich, A.V.; Klein, H.C.; Oude Voshaar, R.C.; Ruhé, E.G.; Eisel, U.L.; Schoevers, R.A. Biomarker approaches in major depressive disorder evaluated in the context of current hypotheses. Biomarkers Med., 2015, 9(3), 277-297.
[http://dx.doi.org/10.2217/bmm.14.114] [PMID: 25731213]
[29]
Yang, J.; Zhang, M.; Ahn, H.; Zhang, Q.; Jin, T.B.; Li, I.; Nemesure, M.; Joshi, N.; Jiang, H.; Miller, J.M.; Ogden, R.T.; Petkova, E.; Milak, M.S.; Sublette, M.E.; Sullivan, G.M.; Trivedi, M.H.; Weissman, M.; McGrath, P.J.; Fava, M.; Kurian, B.T.; Pizzagalli, D.A.; Cooper, C.M.; McInnis, M.; Oquendo, M.A.; Mann, J.J.; Parsey, R.V.; DeLorenzo, C. Development and evaluation of a multimodal marker of major depressive disorder. Hum. Brain Mapp., 2018, 39(11), 4420-4439.
[http://dx.doi.org/10.1002/hbm.24282] [PMID: 30113112]
[30]
Takaki, J.; Fujimori, K.; Miura, M.; Suzuki, T.; Sekino, Y.; Sato, K. L-glutamate released from activated microglia down-regulates astrocytic L-glutamate transporter expression in neuroinflammation: The ‘collusion’ hypothesis for increased extracellular L-glutamate concentration in neuroinflammation. J. Neuroinflammation, 2012, 9, 275.
[http://dx.doi.org/10.1186/1742-2094-9-275] [PMID: 23259598]
[31]
Mandour, A.A. In silico modeling as a perspective in developing potential vaccine candidates and therapeutics for COVID-19. Coatings, 2021, 11(11), 1273.
[http://dx.doi.org/10.3390/coatings11111273]
[32]
Singh, A.V.; Rosenkranz, D.; Ansari, M.H.D.; Singh, R.; Kanase, A.; Singh, S.P.; Johnston, B.; Tentschert, J.; Laux, P.; Luch, A. Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction. Adv. Intell. Syst., 2020, 2(12), 2000084.
[http://dx.doi.org/10.1002/aisy.202000084]
[33]
van Buel, E.M.; Meddens, M.J.M.; Arnoldussen, E.A.; van den Heuvel, E.R.; Bohlmeijer, W.C.; den Boer, J.A.; Muller Kobold, A.; Boonmande Winter, L.J.M.; van Rumpt, D.; Timmers, L.F.J.; Veerman, M.F.A.; Kamphuis, J.S.; Gladkevich, A.V.; Schoevers, R.A.; Luiten, P.G.M.; Eisel, U.L.M.; Bosker, F.J.; Klein, H.C. Major depressive disorder is associated with changes in a cluster of serum and urine biomarkers. J. Psychosom. Res., 2019, 125, 109796.
[http://dx.doi.org/10.1016/j.jpsychores.2019.109796] [PMID: 31470255]
[34]
Guo, H.; Ingolia, N.T.; Weissman, J.S.; Bartel, D.P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature, 2010, 466(7308), 835-840.
[http://dx.doi.org/10.1038/nature09267] [PMID: 20703300]
[35]
Singh, A.V.; Maharjan, R.S.; Kanase, A.; Siewert, K.; Rosenkranz, D.; Singh, R.; Laux, P.; Luch, A. Machine-learning-based approach to decode the influence of nano-material properties on their interaction with cells. ACS Appl. Mater. Interfaces, 2021, 13(1), 1943-1955.
[http://dx.doi.org/10.1021/acsami.0c18470] [PMID: 33373205]
[36]
Das Gupta, K.; Shakespear, M.R.; Curson, J.E.B.; Murthy, A.M.V.; Iyer, A.; Hodson, M.P.; Ramnath, D.; Tillu, V.A.; von Pein, J.B.; Reid, R.C.; Tunny, K.; Hohenhaus, D.M.; Moradi, S.V.; Kelly, G.M.; Kobayashi, T.; Gunter, J.H.; Stevenson, A.J.; Xu, W.; Luo, L.; Jones, A.; Johnston, W.A.; Blumenthal, A.; Alexandrov, K.; Collins, B.M.; Stow, J.L.; Fairlie, D.P.; Sweet, M.J. Class IIa histone deacetylases drive toll-like receptor-inducible glycolysis and macrophage inflammatory responses via pyruvate kinase M2. Cell Rep., 2020, 30(8), 2712-2728.e8.
[http://dx.doi.org/10.1016/j.celrep.2020.02.007] [PMID: 32101747]
[37]
Di Liberto, G.; Pantelyushin, S.; Kreutzfeldt, M.; Page, N.; Musardo, S.; Coras, R.; Steinbach, K.; Vincenti, I.; Klimek, B.; Lingner, T.; Salinas, G.; Lin-Marq, N.; Staszewski, O.; Costa Jordão, M.J.; Wagner, I.; Egervari, K.; Mack, M.; Bellone, C.; Blümcke, I.; Prinz, M.; Pinschewer, D.D.; Merkler, D. Neurons under T cell attack coordinate phagocyte-mediated synaptic stripping. Cell, 2018, 175(2), 458-471.e19.
[http://dx.doi.org/10.1016/j.cell.2018.07.049] [PMID: 30173917]
[38]
Jiang, S.; Yang, W.; Qiu, Y.; Chen, H.Z. Identification of novel quantitative traits-associated susceptibility loci for APOE ε 4 non-carriers of Alzheimer’s disease. Curr. Alzheimer Res., 2015, 12(3), 218-227.
[http://dx.doi.org/10.2174/1567205012666150302160145] [PMID: 25731621]
[39]
Wu, P.; Zuo, X.; Deng, H.; Liu, X.; Liu, L.; Ji, A. Roles of long noncoding RNAs in brain development, functional diversification and neurodegenerative diseases. Brain Res. Bull., 2013, 97, 69-80.
[http://dx.doi.org/10.1016/j.brainresbull.2013.06.001] [PMID: 23756188]
[40]
Winston, C.N.; Goetzl, E.J.; Akers, J.C.; Carter, B.S.; Rockenstein, E.M.; Galasko, D.; Masliah, E.; Rissman, R.A. Prediction of conversion from mild cognitive impairment to dementia with neuronally derived blood exosome protein profile. Alzheimers Dement. (Amst.), 2016, 3, 63-72.
[http://dx.doi.org/10.1016/j.dadm.2016.04.001] [PMID: 27408937]
[41]
Chiesa, A.; Crisafulli, C.; Porcelli, S.; Han, C.; Patkar, A.A.; Lee, S.J.; Park, M.H.; Jun, T.Y.; Serretti, A.; Pae, C.U. Influence of GRIA1, GRIA2 and GRIA4 polymorphisms on diagnosis and response to treatment in patients with major depressive disorder. Eur. Arch. Psychiatry Clin. Neurosci., 2012, 262(4), 305-311.
[http://dx.doi.org/10.1007/s00406-011-0270-y] [PMID: 22057216]
[42]
Perlis, R.H.; Laje, G.; Smoller, J.W.; Fava, M.; Rush, A.J.; McMahon, F.J. Genetic and clinical predictors of sexual dysfunction in citalopram-treated depressed patients. Neuropsychopharmacology, 2009, 34(7), 1819-1828.
[http://dx.doi.org/10.1038/npp.2009.4] [PMID: 19295509]
[43]
Lu, H.C.; Tan, Q.; Rousseaux, M.W.; Wang, W.; Kim, J.Y.; Richman, R.; Wan, Y.W.; Yeh, S.Y.; Patel, J.M.; Liu, X.; Lin, T.; Lee, Y.; Fryer, J.D.; Han, J.; Chahrour, M.; Finnell, R.H.; Lei, Y.; Zurita-Jimenez, M.E.; Ahimaz, P.; Anyane-Yeboa, K.; Van Maldergem, L.; Lehalle, D.; Jean-Marcais, N.; Mosca-Boidron, A.L.; Thevenon, J.; Cousin, M.A.; Bro, D.E.; Lanpher, B.C.; Klee, E.W.; Alexander, N.; Bainbridge, M.N.; Orr, H.T.; Sillitoe, R.V.; Ljungberg, M.C.; Liu, Z.; Schaaf, C.P.; Zoghbi, H.Y. Disruption of the ATXN1-CIC complex causes a spectrum of neurobehavioral phenotypes in mice and humans. Nat. Genet., 2017, 49(4), 527-536.
[http://dx.doi.org/10.1038/ng.3808] [PMID: 28288114]
[44]
Rousseaux, M.W.C.; Tschumperlin, T.; Lu, H.C.; Lackey, E.P.; Bondar, V.V.; Wan, Y.W.; Tan, Q.; Adamski, C.J.; Friedrich, J.; Twaroski, K.; Chen, W.; Tolar, J.; Henzler, C.; Sharma, A.; Bajić, A.; Lin, T.; Duvick, L.; Liu, Z.; Sillitoe, R.V.; Zoghbi, H.Y.; Orr, H.T. ATXN1-CIC complex is the primary driver of cerebellar pathology in spinocerebellar ataxia type 1 through a gain-of-function mechanism. Neuron, 2018, 97(6), 1235-1243.e5.
[http://dx.doi.org/10.1016/j.neuron.2018.02.013] [PMID: 29526553]
[45]
Xu, S.; Liu, Y.; Pu, J.; Gui, S.; Zhong, X.; Tian, L.; Song, X.; Qi, X.; Wang, H.; Xie, P. Chronic Stress in a rat model of depression disturbs the Glutamine-Glutamate-GABA cycle in the striatum, hippocampus, and cerebellum. Neuropsychiatr. Dis. Treat., 2020, 16, 557-570.
[http://dx.doi.org/10.2147/NDT.S245282] [PMID: 32158215]
[46]
Mendez-David, I.; Boursier, C.; Domergue, V.; Colle, R.; Falissard, B.; Corruble, E.; Gardier, A.M.; Guilloux, J.P.; David, D.J. Differential peripheral proteomic biosignature of fluoxetine response in a mouse model of anxiety/depression. Front. Cell. Neurosci., 2017, 11, 237.
[http://dx.doi.org/10.3389/fncel.2017.00237] [PMID: 28860968]
[47]
Chen, X.; Long, F.; Cai, B.; Chen, X.; Chen, G. A novel relationship for schizophrenia, bipolar and major depressive disorder Part 5: A hint from chromosome 5 high density association screen. Am. J. Transl. Res., 2017, 9(5), 2473-2491.
[http://dx.doi.org/10.1007/s12035-016-0102-1] [PMID: 28559998]
[48]
Kajiwara, Y.; Akram, A.; Katsel, P.; Haroutunian, V.; Schmeidler, J.; Beecham, G.; Haines, J.L.; Pericak-Vance, M.A.; Buxbaum, J.D. FE65 binds Teashirt, inhibiting expression of the primatespecific caspase-4. PLoS One, 2009, 4(4), e5071.
[http://dx.doi.org/10.1371/journal.pone.0005071] [PMID: 19343227]
[49]
Kapoor, M.; Wang, J.C.; Wetherill, L.; Le, N.; Bertelsen, S.; Hinrichs, A.L.; Budde, J.; Agrawal, A.; Almasy, L.; Bucholz, K.; Dick, D.M.; Harari, O.; Xiaoling, X.; Hesselbrock, V.; Kramer, J.; Nurnberger, J.I., Jr; Rice, J.; Schuckit, M.; Tischfield, J.; Porjesz, B.; Edenberg, H.J.; Bierut, L.; Foroud, T.; Goate, A. Genome-wide survival analysis of age at onset of alcohol dependence in extended high-risk COGA families. Drug Alcohol Depend., 2014, 142, 56-62.
[http://dx.doi.org/10.1016/j.drugalcdep.2014.05.023] [PMID: 24962325]
[50]
Gurney, M.E. Genetic association of phosphodiesterases with human cognitive performance. Front. Mol. Neurosci., 2019, 12, 22.
[http://dx.doi.org/10.3389/fnmol.2019.00022] [PMID: 30800055]
[51]
Todeva-Radneva, A.; Aryutova, K.; Kandilarova, S.; Paunova, R.; Stoyanov, D. The translational potential of non-coding RNAs and Multimodal MRI Data sets as diagnostic and differential diagnostic biomarkers for mood disorders. Curr. Top. Med. Chem., 2021, 21(11), 949-963.
[http://dx.doi.org/10.2174/1568026621666210521144534] [PMID: 34355686]
[52]
Qi, S.; Schumann, G.; Bustillo, J.; Turner, J.A.; Jiang, R.; Zhi, D.; Fu, Z.; Mayer, A.R.; Vergara, V.M.; Silva, R.F.; Iraji, A.; Chen, J.; Damaraju, E.; Ma, X.; Yang, X.; Stevens, M.; Mathalon, D.H.; Ford, J.M.; Voyvodic, J.; Mueller, B.A.; Belger, A.; Potkin, S.G.; Preda, A.; Zhuo, C.; Xu, Y.; Chu, C.; Banaschewski, T.; Barker, G.J.; Bokde, A.L.W.; Quinlan, E.B.; Desrivières, S.; Flor, H.; Grigis, A.; Garavan, H.; Gowland, P.; Heinz, A.; Martinot, J.L.; Paillère Martinot, M.L.; Artiges, E.; Nees, F.; Orfanos, D.P.; Paus, T.; Poustka, L.; Hohmann, S.; Fröhner, J.H.; Smolka, M.N.; Walter, H.; Whelan, R.; Calhoun, V.D.; Sui, J. Reward processing in novelty seekers: A transdiagnostic psychiatric imaging biomarker. Biol. Psychiatry, 2021, 90(8), 529-539.
[http://dx.doi.org/10.1016/j.biopsych.2021.01.011] [PMID: 33875230]

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