Section 4 - Novel Approaches in Brain Imaging

Barnett, JH, Smoller, JW. The genetics of bipolar disorder. Neuroscience. 2009; 164(1): 331–343.CrossRefGoogle ScholarPubMed

McGuffin, P, Rijsdijk, F, Andrew, M et al. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch Gen Psychiatry. 2003; 60(5): 497–502.CrossRefGoogle ScholarPubMed

Jansen, R, Penninx, BWJH, Madar, V, et al. Gene expression in major depressive disorder. Mol Psychiatr. 2016; 21(3): 339–347.Google Scholar

Hamet, P, Tremblay, J. Genetics and genomics of depression. Metabolism. 2005; 54 (5 Suppl 1): 10–15.CrossRefGoogle ScholarPubMed

Chen, G, Henter, ID, Manji, HK. Translational research in bipolar disorder: Emerging insights from genetically based models. Mol Psychiatry. 2010; 15(9): 883–895.CrossRefGoogle ScholarPubMed

Goes, FS. Genetics of bipolar disorder: Recent update and future directions. Psychiatr Clin North Am. 2016; 39(1): 139–155.CrossRefGoogle ScholarPubMed

Bogdan, R, Salmeron, BJ, Carey, CE, et al. Imaging genetics and genomics in psychiatry: A critical review of progress and potential. Biological Psychiatry. 2017; 82(3): 165–175.Google Scholar

Meyer-Lindenberg, A, Weinberger, DR. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci. 2006; 7(10): 818–827.CrossRefGoogle ScholarPubMed

Fusar-Poli, P, Howes, O, Bechdolf, A, Borgwardt, S. Mapping vulnerability to bipolar disorder: a systematic review and meta-analysis of neuroimaging studies. J Psychiatry Neurosci. 2012; 37(3): 170–184.Google Scholar

Piguet, C, Fodoulian, L, Aubry, JM, Vuilleumier, P, Houenou, J. Bipolar disorder: Functional neuroimaging markers in relatives. Neurosci Biobehav Rev. 2015; 57: 284–296.Google Scholar

Dima, D, Roberts, RE, Frangou, S. Connectomic markers of disease expression, genetic risk and resilience in bipolar disorder. Transl Psychiatry. 2016; 6: e706.Google Scholar

Amico, F, Meisenzahl, E, Koutsouleris, N, et al. Structural MRI correlates for vulnerability and resilience to major depressive disorder. J Psychiatry Neurosci. 2011; 36(1): 15–22.Google Scholar

Huang, H, Fan, X, Williamson, DE, Rao, U. White matter changes in healthy adolescents at familial risk for unipolar depression: A diffusion tensor imaging study. Neuropsychopharmacology. 2011; 36(3): 684–691.Google Scholar

Kerner, B. Toward a deeper understanding of the genetics of bipolar disorder. Frontiers in Psychiatry. 2015; 6: 105.CrossRefGoogle Scholar

Sullivan, P, Wray, N, Consortium, PM. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depressive disorder. Eur Neuropsychopharm. 2019; 29: S805.Google Scholar

Direk, N, Williams, S, Smith, JA, et al. An analysis of two genome-wide association meta-analyses identifies a new locus for broad depression phenotype. Biol Psychiat. 2017; 82(5): 322–329.Google Scholar

Hyde, CL, Nagle, MW, Tian, C, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics. 2016 September; 48(9): 1031–1036.Google Scholar

Ikeda, M, Saito, T, Kondo, K, Iwata, N. Genome-wide association studies of bipolar disorder: A systematic review of recent findings and their clinical implications. Psychiatry Clin Neurosci. 2018; 72(2): 52–63.Google Scholar

Leal-Ortiz, S, Waites, CL, Terry-Lorenzo, R, et al. Piccolo modulation of Synapsin1a dynamics regulates synaptic vesicle exocytosis. Journal of Cell Biology. 2008; 181(5): 831–846.CrossRefGoogle ScholarPubMed

Schuhmacher, A, Mossner, R, Hofels, S, et al. PCLO rs2522833 modulates HPA system response to antidepressant treatment in major depressive disorder. Int J Neuropsychoph. 2011; 14(2): 237–245.CrossRefGoogle ScholarPubMed

Igata, R, Katsuki, A, Kakeda, S, et al. PCLO rs2522833-mediated gray matter volume reduction in patients with drug-naive, first-episode major depressive disorder. Transl Psychiatry. 2017 May 30; 7(5): e1140.Google Scholar

Ryan, J, Artero, S, Carriere, I, et al. GWAS-identified risk variants for major depressive disorder: Preliminary support for an association with late-life depressive symptoms and brain structural alterations. Eur Neuropsychopharm. 2016; 26(1): 113–125.CrossRefGoogle ScholarPubMed

Woudstra, S, Bochdanovits, Z, van Tol, MJ, et al. Piccolo genotype modulates neural correlates of emotion processing but not executive functioning. Transl Psychiatry. 2012; 2: e99.CrossRefGoogle Scholar

Woudstra, S, van Tol, MJ, Bochdanovits, Z, et al. Modulatory effects of the piccolo genotype on emotional memory in health and depression. PLoS One. 2013; 8(4): e61494.CrossRefGoogle ScholarPubMed

Schott, BH, Assmann, A, Schmierer, P, et al. Epistatic interaction of genetic depression risk variants in the human subgenual cingulate cortex during memory encoding. Transl Psychiatry. 2014; 4: e372.Google Scholar

Lewis, CM, Ng, MY, Butler, AW, et al. Genome-wide association study of major recurrent depression in the UK population. Am J Psychiat. 2010; 167(8): 949–957.CrossRefGoogle Scholar

Bermingham, R, Carballedo, A, Lisiecka, D, et al. Effect of genetic variant in BICC1 on functional and structural brain changes in depression. Neuropsychopharmacology. 2012; 37(13): 2855–2862.CrossRefGoogle ScholarPubMed

Dannlowski, U, Kugel, H, Grotegerd, D, et al. NCAN cross-disorder risk variant is associated with limbic gray matter deficits in healthy subjects and major depression. Neuropsychopharmacology. 2015; 40(11): 2510–2516.Google Scholar

Kim, BJ, Zaveri, HP, Shchelochkov, OA, et al. An allelic series of mice reveals a role for RERE in the development of multiple organs affected in chromosome 1p36 deletions. Plos One. 2013; 8(2): e57460.Google Scholar

Kakeda, S, Watanabe, K, Katsuki, A, et al. Genetic effects on white matter integrity in drug-naive patients with major depressive disorder: A diffusion tensor imaging study of 17 genetic loci associated with depressive symptoms. Neuropsych Dis Treat. 2019; 15: 375–383.Google Scholar

Han, KM, Won, E, Kang, J, et al. TESC gene-regulating genetic variant (rs7294919) affects hippocampal subfield volumes and parahippocampal cingulum white matter integrity in major depressive disorder. J Psychiat Res. 2017; 93: 20–29.CrossRefGoogle ScholarPubMed

Cui, LL, Gong, XH, Tang, YQ, et al. Relationship between the LHPP gene polymorphism and resting-state brain activity in major depressive disorder. Neural Plasticity. 2016: 9162590.Google Scholar

Ota, M, Hori, H, Sato, N, et al. Effects of ankyrin 3 gene risk variants on brain structures in patients with bipolar disorder and healthy subjects. Psychiatry Clin Neurosci. 2016; 70(11): 498–506.CrossRefGoogle ScholarPubMed

Lippard, ETC, Jensen, KP, Wang, F, et al. Genetic variation of ANK3 is associated with lower white matter structural integrity in bipolar disorder. Mol Psychiatry. 2017; 22(9): 1225.CrossRefGoogle ScholarPubMed

Delvecchio, G, Dima, D, Frangou, S. The effect of ANK3 bipolar-risk polymorphisms on the working memory circuitry differs between loci and according to risk-status for bipolar disorder. Am J Med Genet B Neuropsychiatr Genet. 2015; 168B(3): 188–196.Google Scholar

Tesli, M, Egeland, R, Sonderby, IE, et al. No evidence for association between bipolar disorder risk gene variants and brain structural phenotypes. J Affect Disord. 2013; 151(1): 291–297.Google Scholar

Falls, DL. Neuregulins: Functions, forms, and signaling strategies. Exp Cell Res. 2003; 284(1): 14–30.Google Scholar

Cannon, DM, Walshe, M, Dempster, E, et al. The association of white matter volume in psychotic disorders with genotypic variation in NRG1, MOG and CNP: A voxel-based analysis in affected individuals and their unaffected relatives. Transl Psychiatry. 2012; 2: e167.Google Scholar

Mechelli, A, Prata, DP, Fu, CH, et al. The effects of neuregulin1 on brain function in controls and patients with schizophrenia and bipolar disorder. Neuroimage. 2008; 42(2): 817–826.Google Scholar

Gomez-Ospina, N, Tsuruta, F, Barreto-Chang, O, Hu, L, Dolmetsch, R. The C terminus of the L-type voltage-gated calcium channel Ca(V)1.2 encodes a transcription factor. Cell. 2006; 127(3): 591–606.Google Scholar

Green, EK, Grozeva, D, Jones, I, et al. The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol Psychiatr. 2010; 15(10): 1016–1022.Google Scholar

Krug, A, Nieratschker, V, Markov, V, et al. Effect of CACNA1C rs1006737 on neural correlates of verbal fluency in healthy individuals. Neuroimage. 2010; 49(2): 1831–1836.Google Scholar

Thimm, M, Kircher, T, Kellermann, T, et al. Effects of a CACNA1C genotype on attention networks in healthy individuals. Psychological Medicine. 2011; 41(7): 1551–1561.CrossRefGoogle ScholarPubMed

Backes, H, Dietsche, B, Nagels, A, et al. Genetic variation in CACNA1C affects neural processing in major depression. J Psychiat Res. 2014; 53: 38–46.CrossRefGoogle ScholarPubMed

Soeiro-de-Souza, MG, Lafer, B, Moreno, RA, et al. The CACNA1C risk allele rs1006737 is associated with age-related prefrontal cortical thinning in bipolar I disorder. Transl Psychiatry. 2017; 7(4): e1086.CrossRefGoogle ScholarPubMed

Perrier, E, Pompei, F, Ruberto, G, et al. Initial evidence for the role of CACNA1 C on subcortical brain morphology in patients with bipolar disorder. Eur Psychiatry. 2011; 26(3): 135–137.Google Scholar

Tesli, M, Skatun, KC, Ousdal, OT, et al. CACNA1C risk variant and amygdala activity in bipolar disorder, schizophrenia and healthy controls. PLoS One. 2013; 8(2): e56970.Google Scholar

Dima, D, Jogia, J, Collier, D, et al. Independent modulation of engagement and connectivity of the facial network during affect processing by CACNA1C and ANK3 risk genes for bipolar disorder. JAMA Psychiatry. 2013; 70(12): 1303–1311.Google Scholar

Jogia, J, Ruberto, G, Lelli-Chiesa, G, et al. The impact of the CACNA1C gene polymorphism on frontolimbic function in bipolar disorder. Mol Psychiatry. 2011; 16(11): 1070–1071.Google Scholar

Whalley, HC, McKirdy, J, Romaniuk, L, et al. Functional imaging of emotional memory in bipolar disorder and schizophrenia. Bipolar Disord. 2009; 11(8): 840–856.Google Scholar

Bigos, KL, Mattay, VS, Callicott, JH, et al. Genetic variation in CACNA1C affects brain circuitries related to mental illness. Arch Gen Psychiatry. 2010; 67(9): 939–945.Google Scholar

Tecelao, D, Mendes, A, Martins, D, et al. The effect of psychosis associated CACNA1C, and its epistasis with ZNF804A, on brain function. Genes Brain Behav. 2019; 18(4): e12510.Google Scholar

Mallas, E, Carletti, F, Chaddock, CA, et al. The impact of CACNA1C gene, and its epistasis with ZNF804A, on white matter microstructure in health, schizophrenia and bipolar disorder(1). Genes Brain Behav. 2017; 16(4): 479–488.Google Scholar

Soeiro-de-Souza, MG, Otaduy, MC, Dias, CZ, et al.The impact of the CACNA1C risk allele on limbic structures and facial emotions recognition in bipolar disorder subjects and healthy controls. J Affect Disord. 2012; 141(1): 94–101.Google Scholar

Wolf, C, Mohr, H, Schneider-Axmann, T, et al. CACNA1 C genotype explains interindividual differences in amygdala volume among patients with schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2014; 264(2): 93–102.Google Scholar

Radua, J, Surguladze, SA, Marshall, N, et al. The impact of CACNA1C allelic variation on effective connectivity during emotional processing in bipolar disorder. Mol Psychiatry. 2013; 18(5): 526–527.Google Scholar

Byrne, EM, Carrillo-Roa, T, Penninx, BW, et al. Applying polygenic risk scores to postpartum depression. Arch Womens Ment Health. 2014; 17(6): 519–528.Google Scholar

Levine, ME, Crimmins, EM, Prescott, CA, et al. A polygenic risk score associated with measures of depressive symptoms among older adults. Biodemogr Soc Biol. 2014; 60(2): 199–211.Google Scholar

Mullins, N, Power, RA, Fisher, HL, et al. Polygenic interactions with environmental adversity in the aetiology of major depressive disorder. Psychological Medicine. 2016; 46(4): 759–770.Google Scholar

Holmes, AJ, Lee, PH, Hollinshead, MO, et al. Individual differences in amygdala-medial prefrontal anatomy link negative affect, impaired social functioning, and polygenic depression risk. Journal of Neuroscience. 2012; 32(50): 18087–18100.Google Scholar

Whalley, HC, Sprooten, E, Hackett, S, et al. Polygenic risk and white matter integrity in individuals at high risk of mood disorder. Biol Psychiat. 2013; 74(4): 280–286.Google Scholar

Yuksel, D, Dietsche, B, Forstner, AJ, et al. Polygenic risk for depression and the neural correlates of working memory in healthy subjects. Prog Neuro-Psychoph. 2017; 79: 67–76.Google Scholar

Reus, LM, Shen, X, Gibson, J, et al. Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank. Sci Rep-Uk. 2017; 7: 42140.Google Scholar

Wigmore, EM, Clarke, TK, Howard, DM, et al. Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19762), UK Biobank (n=24048) and the English Longitudinal Study of Ageing (n=5766). Transl Psychiatry. 2017 August 15; 7(8): e1205.Google Scholar

Wang, T, Zhang, X, Li, A, et al. Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. NeuroImage Clinical. 2017; 14: 441–449.Google Scholar

Tesli, M, Kauppi, K, Bettella, F, et al. Altered brain activation during emotional face processing in relation to both diagnosis and polygenic risk of bipolar disorder. PLoS One. 2015; 10(7): e0134202.Google Scholar

Dima, D, Breen, G. Polygenic risk scores in imaging genetics: Usefulness and applications. J Psychopharmacol. 2015; 29(8): 867–871.Google Scholar

Whalley, HC, Papmeyer, M, Sprooten, E, et al. The influence of polygenic risk for bipolar disorder on neural activation assessed using fMRI. Transl Psychiatry. 2012; 2: e130.Google Scholar

Caseras, X, Tansey, KE, Foley, S, Linden, D. Association between genetic risk scoring for schizophrenia and bipolar disorder with regional subcortical volumes. Transl Psychiatry. 2015; 5: e692.Google Scholar

Wang, T, Zhang, X, Li, A, et al. Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. Neuroimage Clin. 2017; 14: 441–449.Google Scholar

Doan, NT, Kaufmann, T, Bettella, F, et al. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin. 2017; 15: 719–731.CrossRefGoogle ScholarPubMed

Whalley, HC, Sprooten, E, Hackett, S, et al. Polygenic risk and white matter integrity in individuals at high risk of mood disorder. Biol Psychiatry. 2013; 74(4): 280–286.Google Scholar

Reus, LM, Shen, X, Gibson, J, et al. Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank. Sci Rep. 2017; 7: 42140.Google Scholar

Sprooten, E, Fleming, KM, Thomson, PA, et al. White matter integrity as an intermediate phenotype: Exploratory genome-wide association analysis in individuals at high risk of bipolar disorder. Psychiatry Res. 2013; 206(2–3): 223–231.CrossRefGoogle ScholarPubMed

Serretti, A, Cusin, C, Benedetti, F, et al. Insomnia improvement during antidepressant treatment and CLOCK gene polymorphism. Am J Med Genet B Neuropsychiatr Genet. 2005; 137B(1): 36–39.Google Scholar

Zai, CC, de Luca, V, Strauss, J, et al. Genetic factors and suicidal behavior. In: Dwivedi, Y, editor. The Neurobiological Basis of Suicide. Frontiers in Neuroscience. Boca Raton (FL) Florida, US: CRC Press/Taylor & Francis; 2012, p. 213.Google Scholar

Kenna, GA, Roder-Hanna, N, Leggio, L, et al. Association of the 5-HTT gene-linked promoter region (5-HTTLPR) polymorphism with psychiatric disorders: review of psychopathology and pharmacotherapy. Pharmgenomics Pers Med. 2012; 5: 19–35.Google Scholar

Smeraldi, E, Benedetti, F, Zanardi, R. Serotonin transporter promoter genotype and illness recurrence in mood disorders. Eur Neuropsychopharmacol. 2002; 12(1): 73–75.Google Scholar

Serretti, A, Kato, M, De Ronchi, D, Kinoshita, T. Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with selective serotonin reuptake inhibitor efficacy in depressed patients. Mol Psychiatry. 2007; 12(3): 247–257.CrossRefGoogle ScholarPubMed

Parra-Uribe, I, Blasco-Fontecilla, H, Garcia-Pares, G, et al. Risk of re-attempts and suicide death after a suicide attempt: A survival analysis. BMC Psychiatry. 2017; 17(1): 163.Google Scholar

Russ, MJ, Lachman, HM, Kashdan, T, Saito, T, Bajmakovic-Kacila, S. Analysis of catechol-O-methyltransferase and 5-hydroxytryptamine transporter polymorphisms in patients at risk for suicide. Psychiatry Res. 2000; 93(1): 73–78.Google Scholar

Murphy, SE, Norbury, R, Godlewska, BR, et al. The effect of the serotonin transporter polymorphism (5-HTTLPR) on amygdala function: A meta-analysis. Mol Psychiatry. 2013; 18(4): 512–520.Google Scholar

Munafo, MR, Brown, SM, Hariri, AR. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: A meta-analysis. Biol Psychiatry. 2008; 63(9): 852–857.CrossRefGoogle ScholarPubMed

Pezawas, L, Meyer-Lindenberg, A, Drabant, EM, et al. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: A genetic susceptibility mechanism for depression. Nat Neurosci. 2005; 8(6): 828–834.Google Scholar

Jonassen, R, Landro, NI. Serotonin transporter polymorphisms (5-HTTLPR) in emotion processing: Implications from current neurobiology. Prog Neurobiol. 2014; 117: 41–53.Google Scholar

Frodl, T, Zill, P, Baghai, T, et al. Reduced hippocampal volumes associated with the long variant of the tri- and diallelic serotonin transporter polymorphism in major depression. American Journal of Medical Genetics Part B, Neuropsychiatric Genetics: The Official Publication of the International Society of Psychiatric Genetics. 2008; 147B(7): 1003–1007.Google Scholar

Frodl, T, Meisenzahl, EM, Zill, P, et al. Reduced hippocampal volumes associated with the long variant of the serotonin transporter polymorphism in major depression. Arch Gen Psychiat. 2004; 61(2): 177–183.CrossRefGoogle Scholar

Taylor, WD, Steffens, DC, Payne, ME, et al. Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression. Arch Gen Psychiat. 2005; 62(5): 537–544.Google Scholar

Eker, MC, Kitis, O, Okur, H, et al. Smaller hippocampus volume is associated with short variant of 5-HTTLPR polymorphism in medication-free major depressive disorder patients. Neuropsychobiology. 2011; 63(1): 22–28.CrossRefGoogle ScholarPubMed

Hickie, IB, Naismith, SL, Ward, PB, et al. Serotonin transporter gene status predicts caudate nucleus but not amygdala or hippocampal volumes in older persons with major depression. J Affect Disord. 2007; 98(1–2): 137–142.CrossRefGoogle ScholarPubMed

Jaworska, N, MacMaster, FP, Foster, J, Ramasubbu, R. The influence of 5-HTTLPR and Val66 Met polymorphisms on cortical thickness and volume in limbic and paralimbic regions in depression: A preliminary study. BMC Psychiatry. 2016; 16: 61.Google Scholar

Ahdidan, J, Foldager, L, Rosenberg, R, et al. Hippocampal volume and serotonin transporter polymorphism in major depressive disorder. Acta Neuropsychiatr. 2013; 25(4): 206–214.Google Scholar

Cole, J, Weinberger, DR, Mattay, VS, et al. No effect of 5HTTLPR or BDNF Val66 Met polymorphism on hippocampal morphology in major depression. Genes, Brain, and Behavior. 2011; 10(7): 756–764.CrossRefGoogle ScholarPubMed

Hickie, IB, Naismith, SL, Ward, PB, et al. Serotonin transporter gene status predicts caudate nucleus but not amygdala or hippocampal volumes in older persons with major depression. J Affect Disorders. 2007; 98(1–2): 137–142.Google Scholar

Taylor, WD, Macfall, JR, Payne, ME, et al. Orbitofrontal cortex volume in late life depression: Influence of hyperintense lesions and genetic polymorphisms. Psychological Medicine. 2007; 37(12): 1763–1773.Google Scholar

Costafreda, SG, McCann, P, Saker, P, et al. Modulation of amygdala response and connectivity in depression by serotonin transporter polymorphism and diagnosis. J Affect Disorders. 2013; 150(1): 96–103.Google Scholar

Dannlowski, U, Ohrmann, P, Bauer, J, et al. 5-HTTLPR biases amygdala activity in response to masked facial expressions in major depression. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology. 2008; 33(2): 418–424.Google Scholar

Friedel, E, Schlagenhauf, F, Sterzer, P, et al. 5-HTT genotype effect on prefrontal-amygdala coupling differs between major depression and controls. Psychopharmacology. 2009; 205(2): 261–271.Google Scholar

Benedetti, F, Bollettini, I, Poletti, S, et al. White matter microstructure in bipolar disorder is influenced by the serotonin transporter gene polymorphism 5-HTTLPR. Genes Brain Behav. 2015; 14(3): 238–250.CrossRefGoogle ScholarPubMed

Frodl, T, Reinhold, E, Koutsouleris, N, et al. Childhood stress, serotonin transporter gene and brain structures in major depression. Neuropsychopharmacology: Official publication of the American College of Neuropsychopharmacology. 2010; 35(6): 1383–1390.Google Scholar

Alexopoulos, GS, Murphy, CF, Gunning-Dixon, FM, et al. Serotonin transporter polymorphisms, microstructural white matter abnormalities and remission of geriatric depression. J Affect Disorders. 2009; 119(1–3): 132–141.Google Scholar

Parsey, RV, Olvet, DM, Oquendo, MA, et al. Higher 5-HT1A receptor binding potential during a major depressive episode predicts poor treatment response: Preliminary data from a naturalistic study. Neuropsychopharmacology. 2006; 31(8): 1745–1749.Google Scholar

Lemonde, S, Turecki, G, Bakish, D, et al. Impaired repression at a 5-hydroxytryptamine 1 A receptor gene polymorphism associated with major depression and suicide. The Journal of Neuroscience. 2003; 23(25): 8788–8799.Google Scholar

Vai, B, Riberto, M, Ghiglino, D, et al. A 5-HT1Areceptor promoter polymorphism influences fronto-limbic functional connectivity and depression severity in bipolar disorder. Psychiatry Res Neuroimaging. 2017; 270: 1–7.Google Scholar

Bilder, RM, Volavka, J, Lachman, HM, Grace, AA. The catechol-O-methyltransferase polymorphism: Relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes. Neuropsychopharmacology. 2004; 29(11): 1943–1961.Google Scholar

Benedetti, F, Dallaspezia, S, Colombo, C, et al. Effect of catechol-O-methyltransferase Val(108/158)Met polymorphism on antidepressant efficacy of fluvoxamine. Eur Psychiatry. 2010; 25(8): 476–478.CrossRefGoogle ScholarPubMed

Benedetti, F, Barbini, B, Bernasconi, A, et al. Acute antidepressant response to sleep deprivation combined with light therapy is influenced by the catechol-O-methyltransferase Val(108/158)Met polymorphism. J Affect Disord. 2010; 121(1–2): 68–72.Google Scholar

Benedetti, F, Dallaspezia, S, Locatelli, C, et al. Recurrence of bipolar mania is associated with catechol-O-methyltransferase Val(108/158)Met polymorphism. J Affect Disord. 2011; 132(1–2): 293–296.Google Scholar

Benedetti, F, Dallaspezia, S, Colombo, C, et al. Association between catechol-O-methyltransferase Val(108/158)Met polymorphism and psychotic features of bipolar disorder. J Affect Disord. 2010; 125(1–3): 341–344.Google Scholar

Papolos, DF, Veit, S, Faedda, GL, Saito, T, Lachman, HM. Ultra-ultra rapid cycling bipolar disorder is associated with the low activity catecholamine-O-methyltransferase allele. Mol Psychiatry. 1998; 3(4): 346–349.CrossRefGoogle ScholarPubMed

Seok, JH, Choi, S, Lim, HK, et al. Effect of the COMT val158 met polymorphism on white matter connectivity in patients with major depressive disorder. Neurosci Lett. 2013; 545: 35–39.CrossRefGoogle Scholar

Hayashi, K, Yoshimura, R, Kakeda, S, et al. COMT Val158 Met, but not BDNF Val66 Met, is associated with white matter abnormalities of the temporal lobe in patients with first-episode, treatment-naive major depressive disorder: A diffusion tensor imaging study. Neuropsych Dis Treat. 2014; 10: 1183–1190.Google Scholar

Watanabe, K, Kakeda, S, Yoshimura, R, et al. Relationship between the catechol-O-methyl transferase Va1108/158 Met genotype and brain volume in treatment-naive major depressive disorder: Voxel-based morphometry analysis. Psychiat Res-Neuroim. 2015; 233(3): 481–487.Google Scholar

Opmeer, EM, Kortekaas, R, van Tol, MJ, et al. Influence of COMT val158 met genotype on the depressed brain during emotional processing and working memory. PLoS One. 2013; 8(9): e73290.Google Scholar

Vai, B, Riberto, M, Poletti, S, et al. Catechol-O-methyltransferase Val (108/158) Met polymorphism affects fronto-limbic connectivity during emotional processing in bipolar disorder. European Psychiatry. 2017; 41: 53–59.Google Scholar

Miskowiak, KW, Kjaerstad, HL, Stottrup, MM, et al. The catechol-O-methyltransferase (COMT) Val158 Met genotype modulates working memory-related dorsolateral prefrontal response and performance in bipolar disorder. Bipolar Disord. 2017; 19(3): 214–224.CrossRefGoogle Scholar

Hempstead, BL. Brain-derived neurotrophic factor: Three ligands, many actions. Trans Am Clin Climatol Assoc. 2015; 126: 9–19.Google ScholarPubMed

Notaras, M, Hill, R, van den Buuse, M. The BDNF gene Val66 Met polymorphism as a modifier of psychiatric disorder susceptibility: Progress and controversy. Mol Psychiatry. 2015; 20(8): 916–930.Google Scholar

Ide, S, Kakeda, S, Watanabe, K, et al. Relationship between a BDNF gene polymorphism and the brain volume in treatment-naive patients with major depressive disorder: A VBM analysis of brain MRI. Psychiatry Res. 2015; 233(2): 120–124.Google Scholar

Legge, RM, Sendi, S, Cole, JH, et al. Modulatory effects of brain-derived neurotrophic factor Val66 Met polymorphism on prefrontal regions in major depressive disorder. Br J Psychiatry. 2015; 206(5): 379–384.Google Scholar

Frodl, T, Schule, C, Schmitt, G, et al. Association of the brain-derived neurotrophic factor Val66 Met polymorphism with reduced hippocampal volumes in major depression. Arch Gen Psychiatry. 2007; 64(4): 410–416.CrossRefGoogle Scholar

Carballedo, A, Morris, D, Zill, P, et al. Brain-derived neurotrophic factor Val66 Met polymorphism and early life adversity affect hippocampal volume. Am J Med Genet B Neuropsychiatr Genet. 2013; 162B(2): 183–190.Google Scholar

Youssef, MM, Underwood, MD, Huang, YY, et al. Association of BDNF Val66 Met polymorphism and brain BDNF levels with major depression and suicide. Int J Neuropsychopharmacol. 2018; 21(6): 528–538.Google Scholar

Alexopoulos, GS, Glatt, CE, Hoptman, MJ, et al. BDNF val66 met polymorphism, white matter abnormalities and remission of geriatric depression. J Affect Disord. 2010; 125(1–3): 262–268.Google Scholar

Han, KM, Choi, S, Kim, A, et al. The effects of 5-HTTLPR and BDNF Val66 Met polymorphisms on neurostructural changes in major depressive disorder. Psychiatry Res Neuroimaging. 2018; 273: 25–34.Google Scholar

Tatham, EL, Hall, GBC, Clark, D, Foster, J, Ramasubbu, R. The 5-HTTLPR and BDNF polymorphisms moderate the association between uncinate fasciculus connectivity and antidepressants treatment response in major depression. European Archives of Psychiatry and Clinical Neuroscience. 2017; 267(2): 135–147.CrossRefGoogle ScholarPubMed

Yin, YY, Hou, ZH, Wang, XQ, Sui, YX, Yuan, YG. The BDNF Val66 Met polymorphism, resting-state hippocampal functional connectivity and cognitive deficits in acute late-onset depression. J Affect Disorders. 2015; 183: 22–30.Google Scholar

Opmeer, EM, Kortekaas, R, van Tol, MJ, et al. Influence of COMT val158 met genotype on the depressed brain during emotional processing and working memory. Plos One. 2013 September 12; 8(9): e73290.Google Scholar

Gonul, AS, Kitis, O, Eker, MC, et al. Association of the brain-derived neurotrophic factor Val66 Met polymorphism with hippocampus volumes in drug-free depressed patients. World J Biol Psychia. 2011; 12(2): 110–118.Google Scholar

Kanellopoulos, D, Gunning, FM, Morimoto, SS, et al. Hippocampal volumes and the brain-derived neurotrophic factor val66 met polymorphism in geriatric major depression. Am J Geriatr Psychiatry. 2011; 19(1): 13–22.CrossRefGoogle Scholar

Cao, B, Bauer, IE, Sharma, AN, et al. Reduced hippocampus volume and memory performance in bipolar disorder patients carrying the BDNF val66 met met allele. J Affect Disord. 2016; 198: 198–205.Google Scholar

Chepenik, LG, Fredericks, C, Papademetris, X, et al. Effects of the brain-derived neurotrophic growth factor val66 met variation on hippocampus morphology in bipolar disorder. Neuropsychopharmacology. 2009; 34(4): 944–951.CrossRefGoogle Scholar

Matsuo, K, Walss-Bass, C, Nery, FG, et al. Neuronal correlates of brain-derived neurotrophic factor Val66 Met polymorphism and morphometric abnormalities in bipolar disorder. Neuropsychopharmacology. 2009; 34(8): 1904–1913.Google Scholar

Mirakhur, A, Moorhead, TW, Stanfield, AC, et al. Changes in gyrification over 4 years in bipolar disorder and their association with the brain-derived neurotrophic factor valine(66) methionine variant. Biol Psychiatry. 2009; 66(3): 293–297.Google Scholar

Zeni, CP, Mwangi, B, Cao, B, et al. Interaction between BDNF rs6265 Met allele and low family cohesion is associated with smaller left hippocampal volume in pediatric bipolar disorder. J Affect Disord. 2016; 189: 94–97.CrossRefGoogle ScholarPubMed

Mandolini, GM, Lazzaretti, M, Pigoni, A, et al. The impact of BDNF Val66 Met polymorphism on cognition in bipolar disorder: A review: Special section on “translational and neuroscience studies in affective disorders” section editor, Maria Nobile MD, PhD. This section of JAD focuses on the relevance of translational and neuroscience studies in providing a better understanding of the neural basis of affective disorders. The main aim is to briefly summaries relevant research findings in clinical neuroscience with particular regards to specific innovative topics in mood and anxiety disorders. J Affect Disord. 2019; 243: 552–558.Google Scholar

Fernandes, BS, Molendijk, ML, Kohler, CA, et al. Peripheral brain-derived neurotrophic factor (BDNF) as a biomarker in bipolar disorder: a meta-analysis of 52 studies. BMC Med. 2015; 13: 289.Google Scholar

Chen, J, Xu, Y, Zhang, J, et al. Genotypic association of the DAOA gene with resting-state brain activity in major depression. Mol Neurobiol. 2012; 46(2): 361–373.CrossRefGoogle ScholarPubMed

Choi, S, Han, KM, Kang, J, et al. Effects of a polymorphism of the neuronal amino acid transporter SLC6A15 gene on structural integrity of white matter tracts in major depressive disorder. Plos One. 2016 October 10; 11(10): e0164301.Google Scholar

Wang, LJ, Liu, ZF, Cao, XH, et al. A combined study of SLC6A15 gene polymorphism and the resting-state functional magnetic resonance imaging in first-episode drug-naive major depressive disorder. Genet Test Mol Bioma. 2017; 21(9): 523–530.CrossRefGoogle ScholarPubMed

Poletti, S, Riberto, M, Vai, B, et al. A glutamate transporter EAAT1 gene variant influences amygdala functional connectivity in bipolar disorder. Journal of Molecular Neuroscience. 2018; 65(4): 536–545.CrossRefGoogle ScholarPubMed

Poletti, S, Bollettini, I, Lorenzi, C, et al. White matter microstructure in bipolar disorder is influenced by the interaction between a glutamate transporter EAAT1 gene variant and early stress. Molecular Neurobiology. 2018: 1–9.Google Scholar

Benedetti, F, Poletti, S, Locatelli, C, et al. A Homer 1 gene variant influences brain structure and function, lithium effects on white matter, and antidepressant response in bipolar disorder: A multimodal genetic imaging study. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2018; 81: 88–95.CrossRefGoogle Scholar

Benedetti, F, Poletti, S, Locatelli, C, et al. A Homer 1 gene variant influences brain structure and function, lithium effects on white matter, and antidepressant response in bipolar disorder: A multimodal genetic imaging study. Prog Neuropsychopharmacol Biol Psychiatry. 2018; 81: 88–95.CrossRefGoogle Scholar

Bollettini, I, Melloni, EMT, Aggio, V, et al. Clock genes associate with white matter integrity in depressed bipolar patients. Chronobiology International. 2017; 34(2): 212–224.Google Scholar

Poletti, S, Aggio, V, Bollettini, I, et al. SREBF-2 polymorphism influences white matter microstructure in bipolar disorder. Psychiatry Research: Neuroimaging. 2016; 257: 39–46.Google Scholar

Grimes, CA, Jope, RS. CREB DNA binding activity is inhibited by glycogen synthase kinase-3 beta and facilitated by lithium. J Neurochem. 2001; 78(6): 1219–1232.Google Scholar

Inkster, B, Nichols, TE, Saemann, PG, et al. Association of GSK3beta polymorphisms with brain structural changes in major depressive disorder. Arch Gen Psychiatry. 2009; 66(7): 721–728.Google Scholar

Inkster, B, Simmons, A, Cole, JH, et al. Unravelling the GSK3 beta-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder. Psychiatric Genetics. 2018; 28(5): 77–84.Google Scholar

Liu, Z, Guo, H, Cao, XH, et al. A combined study of GSK3 beta polymorphisms and brain network topological metrics in major depressive disorder. Psychiat Res-Neuroim. 2014; 223(3): 210–217.Google Scholar

Inkster, B, Nichols, TE, Saemann, PG, et al. Pathway-based approaches to imaging genetics association studies: Wnt signaling, GSK3beta substrates and major depression. Neuroimage. 2010; 53(3): 908–917.Google Scholar

Benedetti, F, Serretti, A, Colombo, C, et al. A glycogen synthase kinase 3-beta promoter gene single nucleotide polymorphism is associated with age at onset and response to total sleep deprivation in bipolar depression. Neurosci Lett. 2004; 368(2): 123–126.Google Scholar

Benedetti, F, Bollettini, I, Barberi, I, et al. Lithium and GSK3-β promoter gene variants influence white matter microstructure in bipolar disorder. Neuropsychopharmacology. 2013; 38(2): 313.Google Scholar

Horstmann, S, Lucae, S, Menke, A, et al. Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment. Neuropsychopharmacology. 2010; 35(3): 727–740.Google Scholar

Binder, EB, Salyakina, D, Lichtner, P, et al. Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment. Nat Genet. 2004; 36(12): 1319–1325.Google Scholar

Pace, TW, Miller, AH. Cytokines and glucocorticoid receptor signaling. Relevance to major depression. Ann N Y Acad Sci. 2009; 1179: 86–105.Google Scholar

Cordova-Palomera, A, de Reus, MA, Fatjo-Vilas, M, et al. FKBP5 modulates the hippocampal connectivity deficits in depression: A study in twins. Brain Imaging Behav. 2017; 11(1): 62–75.Google Scholar

Tozzi, L, Carballedo, A, Wetterling, F, et al. Single-nucleotide polymorphism of the FKBP5 gene and childhood maltreatment as predictors of structural changes in brain areas involved in emotional processing in depression. Neuropsychopharmacology. 2016; 41(2): 487–497.CrossRefGoogle ScholarPubMed

Han, KM, Won, E, Sim, Y, et al. Influence of FKBP5 polymorphism and DNA methylation on structural changes of the brain in major depressive disorder. Sci Rep-Uk. 2017; 7.Google Scholar

Tozzi, L, Doolin, K, Farrel, C, et al. Functional magnetic resonance imaging correlates of emotion recognition and voluntary attentional regulation in depression: A generalized psycho-physiological interaction study. J Affect Disorders. 2017; 208: 535–544.Google Scholar

Qiu, AQ, Taylor, WD, Zhao, Z, et al. APOE related hippocampal shape alteration in geriatric depression. Neuroimage. 2009; 44(3): 620–626.Google Scholar

Kim, DH, Payne, ME, Levy, RM, MacFall, JR, Steffens, DC. APOE genotype and hippocampal volume change in geriatric depression. Biol Psychiat. 2002; 51(5): 426–429.CrossRefGoogle ScholarPubMed

Sachs-Ericsson, N, Sawyer, K, Corsentino, E, Collins, N, Steffens, DC. The moderating effect of the APOE epsilon 4 allele on the relationship between hippocampal volume and cognitive decline in older depressed patients. Am J Geriat Psychiat. 2011; 19(1): 23–32.Google Scholar

Yuan, YG, Zhang, ZJ, Bai, F, et al. Genetic variation in apolipoprotein E alters regional gray matter volumes in remitted late-onset depression. J Affect Disorders. 2010; 121(3): 273–277.Google Scholar

Shu, H, Yuan, YG, Xie, CM, et al. Imbalanced hippocampal functional networks associated with remitted geriatric depression and apolipoprotein E epsilon 4 allele in nondemented elderly: A preliminary study. J Affect Disorders. 2014; 164: 5–13.Google Scholar

Wu, D, Yuan, YG, Ba, F, et al. Abnormal functional connectivity of the default mode network in remitted late-onset depression. J Affect Disorders. 2013; 147(1–3): 277–287.Google Scholar

Chang, KJ, Hong, CH, Lee, KS, et al. Differential effects of white matter hyperintensity on geriatric depressive symptoms according to APOE-epsilon 4 status. J Affect Disorders. 2015; 188: 28–34.Google Scholar

Lavretsky, H, Lesser, IM, Wohl, M, et al. Apolipoprotein-E and white matter hyperintensities in late-life depression. Am J Geriat Psychiat. 2000; 8(3): 257–261.Google Scholar

Turecki, G, Ota, VK, Belangero, SI, Jackowski, A, Kaufman, J. Early life adversity, genomic plasticity, and psychopathology. Lancet Psychiatry. 2014; 1(6): 461–466.Google Scholar

Lutz, PE, Turecki, G. DNA methylation and childhood maltreatment: From animal models to human studies. Neuroscience. 2014; 264: 142–156.Google Scholar

Szyf, M. The early life social environment and DNA methylation: DNA methylation mediating the long-term impact of social environments early in life. Epigenetics. 2011; 6(8): 971–978.Google Scholar

Caspi, A, Hariri, AR, Holmes, A, Uher, R, Moffitt, TE. Genetic sensitivity to the environment: The case of the serotonin transporter gene and its implications for studying complex diseases and traits. Am J Psychiatry. 2010; 167(5): 509–527.Google Scholar

Philibert, RA, Sandhu, H, Hollenbeck, N, et al. The relationship of 5HTT (SLC6A4) methylation and genotype on mRNA expression and liability to major depression and alcohol dependence in subjects from the Iowa adoption studies. Am J Med Genet B Neuropsychiatr Genet. 2008; 147B(5): 543–549.Google Scholar

Okada, S, Morinobu, S, Fuchikami, M, et al. The potential of SLC6A4 gene methylation analysis for the diagnosis and treatment of major depression. J Psychiatr Res. 2014; 53: 47–53.Google Scholar

Domschke, K, Tidow, N, Schwarte, K, et al. Serotonin transporter gene hypomethylation predicts impaired antidepressant treatment response. Int J Neuropsychopharmacol. 2014; 17(8): 1167–1176.Google Scholar

Olsson, CA, Foley, DL, Parkinson-Bates, M, et al. Prospects for epigenetic research within cohort studies of psychological disorder: A pilot investigation of a peripheral cell marker of epigenetic risk for depression. Biol Psychol. 2010; 83(2): 159–165.Google Scholar

Kang, HJ, Kim, JM, Stewart, R, et al. Association of SLC6A4 methylation with early adversity, characteristics and outcomes in depression. Prog Neuropsychopharmacol Biol Psychiatry. 2013; 44: 23–28.Google Scholar

Sugawara, H, Iwamoto, K, Bundo, M, et al. Hypermethylation of serotonin transporter gene in bipolar disorder detected by epigenome analysis of discordant monozygotic twins. Transl Psychiatry. 2011; 1: e24.CrossRefGoogle ScholarPubMed

Won, E, Choi, S, Kang, J, et al. Association between reduced white matter integrity in the corpus callosum and serotonin transporter gene DNA methylation in medication-naive patients with major depressive disorder. Transl Psychiatry. 2016; 6(8): e866.CrossRefGoogle ScholarPubMed

Booij, L, Szyf, M, Carballedo, A, et al. DNA methylation of the serotonin transporter gene in peripheral cells and stress-related changes in hippocampal volume: A study in depressed patients and healthy controls. PLoS One. 2015; 10(3): e0119061.Google Scholar

Kaer, K, Speek, M. Retroelements in human disease. Gene. 2013; 518(2): 231–241.Google Scholar

Schneider, I, Kugel, H, Redlich, R, et al. Association of serotonin transporter gene AluJb methylation with major depression, amygdala responsiveness, 5-HTTLPR/rs25531 polymorphism, and stress. Neuropsychopharmacology. 2018; 43(6): 1308–1316.Google Scholar

Na, KS, Won, E, Kang, J, et al. Differential effect of COMT gene methylation on the prefrontal connectivity in subjects with depression versus healthy subjects. Neuropharmacology. 2018; 137: 59–70.Google Scholar

Tyrka, AR, Ridout, KK, Parade, SH. Childhood adversity and epigenetic regulation of glucocorticoid signaling genes: Associations in children and adults. Dev Psychopathol. 2016; 28(4pt2): 1319–1331.Google Scholar

Tozzi, L, Farrell, C, Booij, L, et al. Epigenetic changes of FKBP5 as a link connecting genetic and environmental risk factors with structural and functional brain changes in major depression. Neuropsychopharmacology. 2018; 43(5): 1138–1145.Google Scholar

Han, KM, Won, E, Sim, Y, et al. Influence of FKBP5 polymorphism and DNA methylation on structural changes of the brain in major depressive disorder. Sci Rep. 2017; 7: 42621.Google Scholar

Na, KS, Chang, HS, Won, E, et al. Association between glucocorticoid receptor methylation and hippocampal subfields in major depressive disorder. PLoS One. 2014; 9(1): e85425.Google Scholar

Palma-Gudiel, H, Cordova-Palomera, A, Tornador, C, et al. Increased methylation at an unexplored glucocorticoid responsive element within exon 1D of NR3C1 gene is related to anxious-depressive disorders and decreased hippocampal connectivity. Eur Neuropsychopharmacol. 2018; 28(5): 579–588.CrossRefGoogle ScholarPubMed

Fuchikami, M, Morinobu, S, Segawa, M, et al. DNA methylation profiles of the brain-derived neurotrophic factor (BDNF) gene as a potent diagnostic biomarker in major depression. PLoS One. 2011; 6(8): e23881.CrossRefGoogle ScholarPubMed

Tadic, A, Muller-Engling, L, Schlicht, KF, et al. Methylation of the promoter of brain-derived neurotrophic factor exon IV and antidepressant response in major depression. Mol Psychiatry. 2014; 19(3): 281–283.Google Scholar

Kang, HJ, Kim, JM, Lee, JY, et al. BDNF promoter methylation and suicidal behavior in depressive patients. J Affect Disord. 2013; 151(2): 679–685.CrossRefGoogle ScholarPubMed

Martinowich, K, Hattori, D, Wu, H, et al. DNA methylation-related chromatin remodeling in activity-dependent BDNF gene regulation. Science. 2003; 302(5646): 890–893.Google Scholar

Choi, S, Han, KM, Won, E, et al. Association of brain-derived neurotrophic factor DNA methylation and reduced white matter integrity in the anterior corona radiata in major depression. J Affect Disord. 2015; 172: 74–80.Google Scholar

Han, KM, Won, E, Kang, J, et al. TESC gene-regulating genetic variant (rs7294919) affects hippocampal subfield volumes and parahippocampal cingulum white matter integrity in major depressive disorder. J Psychiatr Res. 2017; 93: 20–29.Google Scholar

Linden, DE. Neurofeedback and networks of depression. Dialogues Clin Neurosci. 2014; 16(1): 103–112.Google Scholar

Esmail, S, Linden, DE. Emotion regulation networks and neurofeedback in depression. Cognitive Sciences. 2011; 6(2): 101.Google Scholar

Linden, DE. Brain Control: Developments in Therapy and Implications for Society. Springer; 2014.Google Scholar

Emmert, K, Kopel, R, Sulzer, J, et al. Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated? Neuroimage. 2016; 124: 806–812.CrossRefGoogle Scholar

Engen, HG, Kanske, P, Singer, T. The neural component-process architecture of endogenously generated emotion. Social Cognitive and Affective Neuroscience. 2016; 12(2): 197–211.Google Scholar

Dörfel, D, Lamke, J-P, Hummel, F, et al. Common and differential neural networks of emotion regulation by detachment, reinterpretation, distraction, and expressive suppression: a comparative fMRI investigation. Neuroimage. 2014; 101: 298–309.Google Scholar

Cohen, JR, Berkman, ET, Lieberman, MD. Intentional and incidental self-control in ventrolateral PFC. In: Donald, T. Stuss and Robert, T. Knight. editors. Principles of Frontal Lobe Function. 2nd edition. Oxford University Press; 2013, pp. 417–40.Google Scholar

Anderson, JS, Ferguson, MA, Lopez-Larson, M, Yurgelun-Todd, D. Connectivity gradients between the default mode and attention control networks. Brain Connectivity. 2011; 1(2): 147–157.Google Scholar

Singh, KD, Fawcett, I. Transient and linearly graded deactivation of the human default-mode network by a visual detection task. Neuroimage. 2008; 41(1): 100–112.CrossRefGoogle ScholarPubMed

Sridharan, D, Levitin, DJ, Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences. 2008; 105(34): 12569–12574.CrossRefGoogle ScholarPubMed

Scharnowski, F, Veit, R, Zopf, R, et al. Manipulating motor performance and memory through real-time fMRI neurofeedback. Biological Psychology. 2015; 108: 85–97.CrossRefGoogle ScholarPubMed

Skottnik, L, Sorger, B, Kamp, T, Linden, D, Goebel, R. Success and failure of controlling the real‐time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum. Brain and Behavior. 2019 March; 9(3): e01240.Google Scholar

Sitaram, R, Ros, T, Stoeckel, L, et al. Closed-loop brain training: The science of neurofeedback. Nature Reviews Neuroscience. 2017; 18(2): 86.Google Scholar

Mehler, DM, Sokunbi, MO, Habes, I, et al. Targeting the affective brain—a randomized controlled trial of real-time fMRI neurofeedback in patients with depression. Neuropsychopharmacology. 2018; 43(13): 2578.Google Scholar

Young, KD, Siegle, GJ, Zotev, V, et al. Randomized clinical trial of real-time fMRI amygdala neurofeedback for major depressive disorder: Effects on symptoms and autobiographical memory recall. American Journal of Psychiatry. 2017 August 1; 174(8): 748–755.Google Scholar

Herwig, U, Kaffenberger, T, Jäncke, L, Brühl, AB. Self-related awareness and emotion regulation. NeuroImage. 2010; 50(2): 734–741.Google Scholar

Beer, JS, Heerey, EA, Keltner, D, Scabini, D, Knight, RT. The regulatory function of self-conscious emotion: Insights from patients with orbitofrontal damage. Journal of Personality and Social Psychology. 2003; 85(4): 594.Google Scholar

Schooler, JW, Smallwood, J, Christoff, K, et al. Meta-awareness, perceptual decoupling and the wandering mind. Trends in Cognitive Sciences. 2011; 15(7): 319–326.Google Scholar

Ernst, J, Böker, H, Hättenschwiler, J, et al. The association of interoceptive awareness and alexithymia with neurotransmitter concentrations in insula and anterior cingulate. Social Cognitive and Affective Neuroscience. 2013; 9(6): 857–863.Google Scholar

Deng, Y, Ma, X, Tang, Q. Brain response during visual emotional processing: An fMRI study of alexithymia. Psychiatry Research: Neuroimaging. 2013; 213(3): 225–229.Google Scholar

Goerlich-Dobre, KS, Bruce, L, Martens, S, Aleman, A, Hooker, CI. Distinct associations of insula and cingulate volume with the cognitive and affective dimensions of alexithymia. Neuropsychologia. 2014; 53: 284–292.Google Scholar

Hogeveen, J, Bird, G, Chau, A, Krueger, F, Grafman, J. Acquired alexithymia following damage to the anterior insula. Neuropsychologia. 2016; 82: 142–148.Google Scholar

Zotev, V, Krueger, F, Phillips, R, et al. Self-regulation of amygdala activation using real-time fMRI neurofeedback. PloS One. 2011; 6(9): e24522.Google Scholar

MacDuffie, KE, MacInnes, J, Dickerson, KC, et al. Single session real-time fMRI neurofeedback has a lasting impact on cognitive behavioral therapy strategies. NeuroImage: Clinical. 2018; 19: 868–875.Google Scholar

Muris, P. Relationships between self-efficacy and symptoms of anxiety disorders and depression in a normal adolescent sample. Personality and Individual Differences. 2002; 32(2): 337–348.CrossRefGoogle Scholar

Kavanagh, DJ, Wilson, PH. Prediction of outcome with group cognitive therapy for depression. Behaviour Research and Therapy. 1989; 27(4): 333–343.Google Scholar

Maddux, JE, Meier, LJ. Self-Efficacy and Depression. Self-Efficacy, Adaptation, and Adjustment. Springer; 1995. 143–169.CrossRefGoogle Scholar

Bartra, O, McGuire, JT, Kable, JW. The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage. 2013; 76: 412–427.Google Scholar

Balleine, BW, Delgado, MR, Hikosaka, O. The role of the dorsal striatum in reward and decision-making. Journal of Neuroscience. 2007; 27(31): 8161–8165.Google Scholar

Young, KD, Siegle, GJ, Misaki, M, et al. Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder. NeuroImage: Clinical. 2018; 17: 691–703.Google Scholar

Sheline, YI, Barch, DM, Price, JL, et al. The default mode network and self-referential processes in depression. Proceedings of the National Academy of Sciences. 2009; 106(6): 1942–1947.Google Scholar

Zhu, X, Wang, X, Xiao, J, et al. Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological Psychiatry. 2012; 71(7): 611–617.Google Scholar

Hamilton, JP, Furman, DJ, Chang, C, et al. Default-mode and task-positive network activity in major depressive disorder: Implications for adaptive and maladaptive rumination. Biological Psychiatry. 2011; 70(4): 327–333.Google Scholar

Marchetti, I, Koster, EH, Sonuga-Barke, EJ, De Raedt, R. The default mode network and recurrent depression: A neurobiological model of cognitive risk factors. Neuropsychology Review. 2012; 22(3): 229–251.Google Scholar

Belleau, EL, Taubitz, LE, Larson, CL. Imbalance of default mode and regulatory networks during externally focused processing in depression. Social Cognitive and Affective Neuroscience. 2014; 10(5): 744–751.Google Scholar

Burkhouse, KL, Jacobs, RH, Peters, AT, et al. Neural correlates of rumination in adolescents with remitted major depressive disorder and healthy controls. Cognitive, Affective, & Behavioral Neuroscience. 2017; 17(2): 394–405.Google Scholar

Mandell, D, Siegle, GJ, Shutt, L, Feldmiller, J, Thase, ME. Neural substrates of trait ruminations in depression. Journal of Abnormal Psychology. 2014; 123(1): 35.CrossRefGoogle ScholarPubMed

Siegle, GJ, Steinhauer, SR, Thase, ME, Stenger, VA, Carter, CS. Can’t shake that feeling: event-related fMRI assessment of sustained amygdala activity in response to emotional information in depressed individuals. Biological Psychiatry. 2002; 51(9): 693–707.CrossRefGoogle ScholarPubMed

Siegle, GJ, Carter, CS, Thase, ME. Use of fMRI to predict recovery from unipolar depression with cognitive behavior therapy. American Journal of Psychiatry. 2006; 163(4): 735–738.Google Scholar

Linden, DE, Habes, I, Johnston, SJ, et al. Real-time self-regulation of emotion networks in patients with depression. PloS One. 2012; 7(6): e38115.Google Scholar

Yuan, H, Young, KD, Phillips, R, et al. Resting-state functional connectivity modulation and sustained changes after real-time functional magnetic resonance imaging neurofeedback training in depression. Brain Connectivity. 2014; 4(9): 690–701.Google Scholar

Hamilton, JP, Glover, GH, Bagarinao, E, et al. Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Research: Neuroimaging. 2016; 249: 91–96.Google Scholar

Teasdale, JD, Green, HA. Ruminative self-focus and autobiographical memory. Personality and Individual Differences. 2004; 36(8): 1933–1943.Google Scholar

Sutherland, K, Bryant, RA. Rumination and overgeneral autobiographical memory. Behaviour Research and Therapy. 2007; 45(10): 2407–2416.Google Scholar

Ramot, M, Grossman, S, Friedman, D, Malach, R. Covert neurofeedback without awareness shapes cortical network spontaneous connectivity. Proceedings of the National Academy of Sciences. 2016; 113(17): E2413–E20.Google Scholar

Thibault, RT, MacPherson, A, Lifshitz, M, Roth, RR, Raz, A. Neurofeedback with fMRI: A critical systematic review. Neuroimage. 2018; 172: 786–807.Google Scholar

Hamann, S, Canli, T. Individual differences in emotion processing. Current Opinion in Neurobiology. 2004; 14(2): 233–238.Google Scholar

Berlim, MT, Van den Eynde, F, Daskalakis, ZJ. Clinically meaningful efficacy and acceptability of low-frequency repetitive transcranial magnetic stimulation (rTMS) for treating primary major depression: a meta-analysis of randomized, double-blind and sham-controlled trials. Neuropsychopharmacology. 2013; 38(4): 543.CrossRefGoogle ScholarPubMed

Berlim, M, Van den Eynde, F, Tovar-Perdomo, S, Daskalakis, Z. Response, remission and drop-out rates following high-frequency repetitive transcranial magnetic stimulation (rTMS) for treating major depression: A systematic review and meta-analysis of randomized, double-blind and sham-controlled trials. Psychological Medicine. 2014; 44(2): 225–239.Google Scholar

Young, KD, Zotev, V, Phillips, R, Misaki, M, Yuan, H, Drevets, WC, et al. Real-time fMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PloS One. 2014; 9(2):e88785.Google Scholar

Yang, Y, Zhong, N, Imamura, K, et al. Task and resting-state fMRI reveal altered salience responses to positive stimuli in patients with major depressive disorder. PLOS ONE. 2016; 11(5): e0155092.Google Scholar

Sheline, YI, Barch, DM, Donnelly, JM, et al. Increased amygdala response to masked emotional faces in depressed subjects resolves with antidepressant treatment: An fMRI study. Biological Psychiatry. 2001; 50(9): 651–658.Google Scholar

Drevets, WC, Price, JL, Bardgett, ME, et al. Glucose metabolism in the amygdala in depression: Relationship to diagnostic subtype and plasma cortisol levels. Pharmacology Biochemistry and Behavior. 2002; 71(3): 431–447.Google Scholar

Drevets, WC. Neuroimaging abnormalities in the amygdala in mood disorders. Annals of the New York Academy of Sciences. 2003; 985(1): 420–444.Google Scholar

Victor, TA, Furey, ML, Fromm, SJ, Öhman, A, Drevets, WC. Relationship between amygdala responses to masked faces and mood state and treatment in major depressive disorder. Archives of General Psychiatry. 2010; 67(11): 1128–1138.Google Scholar

Suslow, T, Konrad, C, Kugel, H, et al. Automatic mood-congruent amygdala responses to masked facial expressions in major depression. Biological Psychiatry. 2010; 67(2): 155–160.Google Scholar

Seeley, WW, Menon, V, Schatzberg, AF, Keller, J, Glover, GH, Kenna, H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience. 2007; 27(9): 2349–56.Google Scholar

Jacobs, R, Barba, A, Gowins, J, et al. Decoupling of the amygdala to other salience network regions in adolescent-onset recurrent major depressive disorder. Psychological Medicine. 2016; 46(5): 1055–1067.Google Scholar

Packard, MG, Cahill, L. Affective modulation of multiple memory systems. Current Opinion in Neurobiology. 2001; 11(6): 752–756.Google Scholar

Burianova, H, McIntosh, AR, Grady, CL. A common functional brain network for autobiographical, episodic, and semantic memory retrieval. Neuroimage. 2010; 49(1): 865–874.Google Scholar

Noulhiane, M, Piolino, P, Hasboun, D, et al. Autobiographical memory after temporal lobe resection: neuropsychological and MRI volumetric findings. Brain. 2007; 130(12): 3184–3199.Google Scholar

Aggleton, JP, Brown, MW. Episodic memory, amnesia, and the hippocampal–anterior thalamic axis. Behavioral and Brain Sciences. 1999; 22(3): 425–444.Google Scholar

Young, KD. Effects of amygdala neurofeedback on depressive symptoms. Identification No. NCT02709161. Retrieved from https://clinicaltrials.gov/ct2/show/NCT02709161. 2016.Google Scholar

Young, KD. Neurofeedback for treatment resistant depression. Identification No. NCT03428828. Retrieved from https://clinicaltrials.gov/ct2/show/NCT03428828. 2018.Google Scholar

Engen, HG, Bernhardt, BC, Skottnik, L, Ricard, M, Singer, T. Structural changes in socio-affective networks: Multi-modal MRI findings in long-term meditation practitioners. Neuropsychologia. 2018; 116: 26–33.Google Scholar

Young, KD, Misaki, M, Harmer, CJ, et al. Real-time functional magnetic resonance imaging amygdala neurofeedback changes positive information processing in major depressive disorder. Biological Psychiatry. 2017; 82(8): 578–586.Google Scholar

Mathiak, K. Symptom based treatment affects brain plasticity – cognitive training in patients with affective symptoms (APIC-II). Identification No. NCT03183947. Retrieved from https://clinicaltrials.gov/ct2/show/NCT03183947. 2017.Google Scholar

Hamilton, JP, Etkin, A, Furman, DJ, et al. Functional neuroimaging of major depressive disorder: A meta-analysis and new integration of baseline activation and neural response data. American Journal of Psychiatry. 2012; 169(7): 693–703.Google Scholar

Caria, A, Sitaram, R, Veit, R, Begliomini, C, Birbaumer, N. Volitional control of anterior insula activity modulates the response to aversive stimuli. A real-time functional magnetic resonance imaging study. Biological Psychiatry. 2010; 68(5): 425–432.Google Scholar

Herwig, U, Lutz, J, Scherpiet, S, et al. Training emotion regulation through real-time fMRI neurofeedback of amygdala activity. NeuroImage. 2019; 184: 687–696.Google Scholar

Perlman, G, Simmons, AN, Wu, J, et al. Amygdala response and functional connectivity during emotion regulation: a study of 14 depressed adolescents. Journal of Affective Disorders. 2012; 139(1): 75–84.Google Scholar

Carballedo, A, Scheuerecker, J, Meisenzahl, E, et al. Functional connectivity of emotional processing in depression. Journal of Affective Disorders. 2011; 134(1–3): 272–279.Google Scholar

de Almeida, JRC, Versace, A, Mechelli, A, et al. Abnormal amygdala-prefrontal effective connectivity to happy faces differentiates bipolar from major depression. Biological Psychiatry. 2009; 66(5): 451–459.Google Scholar

Zahn, R, Weingartner, J, Basilio, R, et al. 30 Blame Rebalance fMRI Feedback Proof-of-Concept Trial in Major Depressive Disorder. BMJ Publishing Group Ltd; 2017.Google Scholar

Greicius, MD, Flores, BH, Menon, V, et al. Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry. 2007; 62(5): 429–437.CrossRefGoogle ScholarPubMed

Sheline, YI, Price, JL, Yan, Z, Mintun, MA. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proceedings of the National Academy of Sciences. 2010; 107(24): 11020–11025.Google Scholar

Veer, IM, Beckmann, C, Van Tol, M-J, et al. Whole brain resting-state analysis reveals decreased functional connectivity in major depression. Frontiers in Systems Neuroscience. 2010; 4: 41.Google Scholar

Drysdale, AT, Grosenick, L, Downar, J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine. 2017; 23(1): 28.Google Scholar

Dinga, R, Schmaal, L, Penninx, B, et al. Evaluating the evidence for biotypes of depression: Methodological replication and extension of. NeuroImage: Clinical. 2019; 22: 101796.CrossRefGoogle ScholarPubMed

Yamada, T, Hashimoto, R-i, Yahata, N, et al. Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: a challenge for developing theranostic biomarkers. International Journal of Neuropsychopharmacology. 2017; 20(10): 769–781.Google Scholar

Moll, J, Weingartner, JH, Bado, P, et al. Voluntary enhancement of neural signatures of affiliative emotion using fMRI neurofeedback. PloS One. 2014; 9(5): e97343.Google Scholar

Sokhadze, EM, El-Baz, AS, Tasman, A, et al. Neuromodulation integrating rTMS and neurofeedback for the treatment of autism spectrum disorder: an exploratory study. Applied Psychophysiology and Biofeedback. 2014; 39(3–4): 237–257.Google Scholar

Koganemaru, S, Mikami, Y, Maezawa, H, et al. Neurofeedback control of the human GABAergic system using non-invasive brain stimulation. Neuroscience. 2018; 380: 38–48.Google Scholar

Siepmann, M, Aykac, V, Unterdörfer, J, Petrowski, K, Mueck-Weymann, M. A pilot study on the effects of heart rate variability biofeedback in patients with depression and in healthy subjects. Applied Psychophysiology and Biofeedback. 2008; 33(4): 195–201.CrossRefGoogle ScholarPubMed

Karavidas, MK, Lehrer, PM, Vaschillo, E, et al. Preliminary results of an open label study of heart rate variability biofeedback for the treatment of major depression. Applied Psychophysiology and Biofeedback. 2007; 32(1): 19–30.CrossRefGoogle ScholarPubMed

Gevirtz, R. The promise of heart rate variability biofeedback: Evidence-based applications. Biofeedback. 2013; 41(3): 110–120.Google Scholar

Tang, Y, Posner, MI, Rothbart, MK. Meditation improves self‐regulation over the life span. Annals of the New York Academy of Sciences. 2014; 1307(1): 104–111.Google Scholar

Tang, Y, Ma, Y, Wang, J, et al. Short-term meditation training improves attention and self-regulation. Proceedings of the National Academy of Sciences. 2007; 104(43): 17152–17156.Google Scholar

Baird, B, Mrazek, MD, Phillips, DT, Schooler, JW. Domain-specific enhancement of metacognitive ability following meditation training. Journal of Experimental Psychology: General. 2014; 143(5): 1972.Google Scholar

Hofmann, SG, Sawyer, AT, Witt, AA, Oh, D. The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. Journal of Consulting and Clinical Psychology. 2010; 78(2): 169.Google Scholar

Peciña, M. Study of neural responses induced by antidepressant effects (SONRISA). Identification No. NCT02674529. Retrieved from https://clinicaltrials.gov/ct2/show/NCT02674529. 2016.Google Scholar

Scharnowski, F. Neural correlates of neurofeedback training. Identification No. NCT03165578. Retrieved from https://clinicaltrials.gov/ct2/show/NCT03165578. 2017.Google Scholar

Sorger, B, Scharnowski, F, Linden, DE, Hampson, M, Young, KD. Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies. NeuroImage. 2019; 186: 256–265.Google Scholar

Randell, E, McNamara, R, Subramanian, L, Hood, K, Linden, D. Current Practices in Clinical Neurofeedback with Functional MRI-Analysis of a Survey Using the TIDieR Checklist. Eur Psychiatry; 2018.Google Scholar

Grossberg, , S, On the dynamics of operant conditioning. Journal of Theoretical Biology. 1971 November; 33(2): 225–255.Google Scholar

Staddon, JER, Cerutti, DT. Operant Conditioning. Annu Rev Psychol. 2003; 54: 115–144.Google Scholar

Jobsis, FF. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science. 1977; 198(4323): 1264–1267.Google Scholar

Villringer, A, Planck, J, Hock, C, Schleinkofer, L, Dirnagl, U. Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults. Neurosci Lett. 1993; 154(1–2): 101–104.Google Scholar

Gratton, G, Maier, JS, Fabiani, M, Mantulin, WW, Gratton, E. Feasibility of intracranial near-infrared optical scanning. Psychophysiology. 1994; 31(2): 211–215.CrossRefGoogle ScholarPubMed

Maki, A, Yamashita, Y, Ito, Y, et al. Spatial and temporal analysis of human motor activity using noninvasive NIR topography. Med Phys. 1995; 22(12): 1997–2005.CrossRefGoogle ScholarPubMed

Villringer, A, Chance, B. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 1997; 20(10): 435–442.Google Scholar

Obrig, H. et al. Near-infrared spectroscopy: Does it function in functional activation studies of the adult brain? Int J Psychophysiol. 2000; 35(2–3): 125–142.CrossRefGoogle ScholarPubMed

Yamashita, Y, Maki, A, Koizumi, H. Wavelength dependence of the precision of noninvasive optical measurement of oxy-, deoxy-, and total-hemoglobin concentration. Med Phys. 2001; 28(6): 1108–1114.Google Scholar

Ferrari, M, Quaresima, V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage. 2012; 63(2): 921–935.Google Scholar

Strangman, G, Boas, DA, Sutton, JP. Non-invasive neuroimaging using near-infrared light. Biol Psychiatry. 2002; 52(7): 679–693.Google Scholar

Elwell, CE. et al. Measurement of adult cerebral haemodynamics using near infrared spectroscopy. Acta Neurochir Suppl (Wien). 1993; 59: 74–80.Google ScholarPubMed

Takahashi, T, Takikawa, Y, Kawagoe, R, et al. Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task. Neuroimage. 2011; 57(3): 991–1002.Google Scholar

Kohri, S, Hoshi, Y, Tamura, M, et al. Quantitative evaluation of the relative contribution ratio of cerebral tissue to near-infrared signals in the adult human head: A preliminary study. Physiol Meas. 2002; 23(2): 301–312.Google Scholar

Fox, PT, Raichle, ME. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proc Natl Acad Sci U S A. 1986; 83(4): 1140–1144.CrossRefGoogle ScholarPubMed

Hock, C. et al. Decrease in parietal cerebral hemoglobin oxygenation during performance of a verbal fluency task in patients with Alzheimer’s disease monitored by means of near-infrared spectroscopy (NIRS)–correlation with simultaneous rCBF-PET measurements. Brain Res. 1997; 755(2): 293–303.Google Scholar

Sato, H. et al. A NIRS-fMRI investigation of prefrontal cortex activity during a working memory task. Neuroimage. 2013; 83: 158–173.Google Scholar

Cui, X, Bray, S, Bryant, DM, Glover, GH, Reiss, AL. A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. Neuroimage. 2011; 54(4): 2808–2821.Google Scholar

Strangman, G, Culver, JP, Thompson, JH, Boas, DA. A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. Neuroimage. 2002; 17(2): 719–731.Google Scholar

Moriguchi, Y. et al. Validation of brain-derived signals in near-infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging. Hum Brain Mapp. 2017; 38(10): 5274–5291.Google Scholar

Tsuzuki, D, Jurcak, V, Singh, AK, et al. Virtual spatial registration of stand-alone fNIRS data to MNI space. Neuroimage. 2007; 34(4): 1506–1518.Google Scholar

Okada, F, Takahashi, N, Tokumitsu, Y. Dominance of the ‘nondominant’ hemisphere in depression. J Affect Disord. 1996; 37(1): 13–21.CrossRefGoogle ScholarPubMed

Cyranoski, D. Neuroscience: Thought experiment. Nature. 2011; 469(7329): 148–149.Google Scholar

Zhang, H. et al. Near-infrared spectroscopy for examination of prefrontal activation during cognitive tasks in patients with major depressive disorder: A meta-analysis of observational studies. Psychiatry Clin Neurosci. 2015; 69(1): 22–33.Google Scholar

Matsuo, K, Kato, T, Fukuda, M, Kato, N. Alteration of hemoglobin oxygenation in the frontal region in elderly depressed patients as measured by near-infrared spectroscopy. J Neuropsychiatry Clin Neurosci. 2000; 12(4): 465–471.Google Scholar

Suto, T, Fukuda, M, Ito, M, Uehara, T, Mikuni, M. Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study. Biol Psychiatry. 2004; 55(5): 501–511.Google Scholar

Hirano, J. et al. Frontal and temporal cortical functional recovery after electroconvulsive therapy for depression: A longitudinal functional near-infrared spectroscopy study. J Psychiatr Res. 2017; 91: 26–35.CrossRefGoogle ScholarPubMed

Koseki, S. et al. The relationship between positive and negative automatic thought and activity in the prefrontal and temporal cortices: A multi-channel near-infrared spectroscopy (NIRS) study. J Affect Disord. 2013; 151(1): 352–359.CrossRefGoogle ScholarPubMed

Noda, T. et al. Frontal and right temporal activations correlate negatively with depression severity during verbal fluency task: A multi-channel near-infrared spectroscopy study. J Psychiatr Res. 2012; 46(7): 905–912.CrossRefGoogle ScholarPubMed

Pu, S. et al. The relationship between the prefrontal activation during a verbal fluency task and stress-coping style in major depressive disorder: A near-infrared spectroscopy study. J Psychiatr Res. 2012; 46(11): 1427–1434.Google Scholar

Pu, S. et al. Reduced frontopolar activation during verbal fluency task associated with poor social functioning in late-onset major depression: Multi-channel near-infrared spectroscopy study. Psychiatry Clin Neurosci. 2008; 62(6): 728–737.Google Scholar

Tsujii, N. et al. Relationship between prefrontal hemodynamic responses and quality of life differs between melancholia and non-melancholic depression. Psychiatry Res. 2016; 253(30): 26–35.Google Scholar

Usami, M, Iwadare, Y, Kodaira, M, Watanabe, K, Saito, K. Near infrared spectroscopy study of the frontopolar hemodynamic response and depressive mood in children with major depressive disorder: A pilot study. PLoS One. 2014; 9(1): e86290.Google Scholar

Akashi, H, Tsujii, N, Mikawa, W, et al. Prefrontal cortex activation is associated with a discrepancy between self- and observer-rated depression severities of major depressive disorder: A multichannel near-infrared spectroscopy study. J Affect Disord. 2015; 174(15): 165–172.Google Scholar

Tsujii, N. et al. Right temporal activation differs between melancholia and nonmelancholic depression: A multichannel near-infrared spectroscopy study. J Psychiatr Res. 2014; 55: 1–7.Google Scholar

Pu, S. et al. Suicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder. J Affect Disord. 2015; 18: 9–17.Google Scholar

Tomioka, H. et al. A longitudinal functional neuroimaging study in medication-naive depression after antidepressant treatment. PLoS One. 2015; 10(3): e0120828.Google Scholar

Masuda, K. et al. Different functioning of prefrontal cortex predicts treatment response after a selective serotonin reuptake inhibitor treatment in patients with major depression. J Affect Disord. 2017; 214: 44–52.Google Scholar

Nishida, M, Kikuchi, S, Matsumoto, K, et al. Sleep complaints are associated with reduced left prefrontal activation during a verbal fluency task in patients with major depression: A multi-channel near-infrared spectroscopy study. J Affect Disord. 2017; 207: 102–109.Google Scholar

Takamiya, A. et al. High-dose antidepressants affect near-infrared spectroscopy signals: A retrospective study. Neuroimage Clin. 2017; 14: 648–655.Google Scholar

Tsujii, N. et al. Reduced left precentral regional responses in patients with major depressive disorder and history of suicide attempts. PLoS One. 2017; 12(4): e0175249.CrossRefGoogle ScholarPubMed

Schlosser, R. et al. Functional magnetic resonance imaging of human brain activity in a verbal fluency task. J Neurol Neurosurg Psychiatry. 1998; 64(4): 492–498.Google Scholar

Cuenod, CA, Bookheimer, SY, Hertz-Pannier, L, et al. Functional MRI during word generation, using conventional equipment: A potential tool for language localization in the clinical environment,. Neurology. 1995; 45(10): 1821–1827.Google Scholar

Strauss, E, Sherman, EMS, Spreen, O, Spreen, O. A Compendium of Neuropsychological Tests : Administration, Norms, and Commentary. Oxford; New York: Oxford University Press, 2006.Google Scholar

Ma, XY. et al. Near-infrared spectroscopy reveals abnormal hemodynamics in the left dorsolateral prefrontal cortex of menopausal depression patients. Dis Markers. 2017; (2017): 1695930. DOI: 10.1155/2017/1695930Google Scholar

Liu, X. et al. Relationship between the prefrontal function and the severity of the emotional symptoms during a verbal fluency task in patients with major depressive disorder: A multi-channel NIRS study. Prog Neuropsychopharmacol Biol Psychiatry. 2014; 54: 114–121.Google Scholar

Pu, S. et al. Reduced prefrontal cortex activation during the working memory task associated with poor social functioning in late-onset depression: Multi-channel near-infrared spectroscopy study. Psychiatry Res. 2012; 203(2–3): 222–228.Google Scholar

Eschweiler, GW. et al. Left prefrontal activation predicts therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) in major depression. Psychiatry Res. 2000; 99(3): 161–172.Google Scholar

Pu, S. et al. A multi-channel near-infrared spectroscopy study of prefrontal cortex activation during working memory task in major depressive disorder. Neurosci Res. 2011; 70(1): 91–97.Google Scholar

Onishi, Y, Kikuchi, S, Watanabe, E, Kato, S. Alterations in prefrontal cortical activity in the course of treatment for late-life depression as assessed on near-infrared spectroscopy. Psychiatry Clin Neurosci. 2008; 62(2): 177–184.Google Scholar

Kikuchi, S. et al. Prefrontal cerebral activity during a simple “rock, paper, scissors” task measured by the noninvasive near-infrared spectroscopy method. Psychiatry Res. 2007; 156(3): 199–208.Google Scholar

Matsuo, K, Kato, N, Kato, T. Decreased cerebral haemodynamic response to cognitive and physiological tasks in mood disorders as shown by near-infrared spectroscopy. Psychol Med. 2002; 32(6): 1029–1037.Google Scholar

Matsuo, K, Onodera, Y, Hamamoto, T, et al. Hypofrontality and microvascular dysregulation in remitted late-onset depression assessed by functional near-infrared spectroscopy. Neuroimage. 2005; 26(1): 234–242.Google Scholar

Ono, Y. et al. Prefrontal oxygenation during verbal fluency and cognitive function in adolescents with bipolar disorder type II. Asian J Psychiatr. 2017; 25: 147–153.Google Scholar

Mikawa, W, Tsujii, N, Akashi, H, et al. Left temporal activation associated with depression severity during a verbal fluency task in patients with bipolar disorder: A multichannel near-infrared spectroscopy study. J Affect Disord. 2015; 173: 193–200.Google Scholar

Nishimura, Y. et al. Social function and frontopolar activation during a cognitive task in patients with bipolar disorder. Neuropsychobiology. 2015; 72(2): 81–90.CrossRefGoogle ScholarPubMed

Nishimura, Y, Takahashi, K, Ohtani, T, et al. Dorsolateral prefrontal hemodynamic responses during a verbal fluency task in hypomanic bipolar disorder. Bipolar Disord. 2015; 17(2): 172–183.Google Scholar

Ono, Y. et al. Reduced prefrontal activation during performance of the Iowa gambling task in patients with bipolar disorder. Psychiatry Res. 2015; 233(1): 1–8.Google Scholar

Matsuo, K. et al. A near-infrared spectroscopy study of prefrontal cortex activation during a verbal fluency task and carbon dioxide inhalation in individuals with bipolar disorder. Bipolar Disord. 2007; 9(8): 876–883.Google Scholar

Matsuo, K, Watanabe, A, Onodera, Y, Kato, N, Kato, T. Prefrontal hemodynamic response to verbal-fluency task and hyperventilation in bipolar disorder measured by multi-channel near-infrared spectroscopy. J Affect Disord. 2004; 82(1): 85–92.Google Scholar

Kubota, Y. et al. Altered prefrontal lobe oxygenation in bipolar disorder: A study by near-infrared spectroscopy. Psychol Med. 2009; 39(8): 1265–1275.CrossRefGoogle ScholarPubMed

Ohi, K. et al. Impact of familial loading on prefrontal activation in major psychiatric disorders: A near-infrared spectroscopy (NIRS) study. Sci Rep. 2017; 7: 44268.Google Scholar

Ohta, H. et al. Hypofrontality in panic disorder and major depressive disorder assessed by multi-channel near-infrared spectroscopy. Depress Anxiety. 2008; 25(12): 1053–1059.Google Scholar

Kameyama, M. et al. Frontal lobe function in bipolar disorder: A multichannel near-infrared spectroscopy study. Neuroimage. 2006; 29(1): 172–184.Google Scholar

Takizawa, R. et al. Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage. 2014; 85(Pt 1): 498–507.Google Scholar

Kinou, M. et al. Differential spatiotemporal characteristics of the prefrontal hemodynamic response and their association with functional impairment in schizophrenia and major depression. Schizophr Res. 2013; 150(2–3): 459–467.Google Scholar

Ohtani, T, Nishimura, Y, Takahashi, K, et al. Association between longitudinal changes in prefrontal hemodynamic responses and social adaptation in patients with bipolar disorder and major depressive disorder. J Affect Disord. 2015; 176: 78–86.Google Scholar

Zhu, Y. et al. Prefrontal activation during a working memory task differs between patients with unipolar and bipolar depression: A preliminary exploratory study. J Affect Disord. 2018 January 1; 225: 64–70.Google Scholar

Matsubara, T. et al. Prefrontal activation in response to emotional words in patients with bipolar disorder and major depressive disorder. Neuroimage. 2014; 85(Pt 1): 489–497.CrossRefGoogle ScholarPubMed

Uceyler, N, Zeller, J, Kewenig, S, et al. Increased cortical activation upon painful stimulation in fibromyalgia syndrome. BMC Neurol. 2015; 15: 210.Google Scholar

Takei, Y. et al. Near-infrared spectroscopic study of frontopolar activation during face-to-face conversation in major depressive disorder and bipolar disorder. J Psychiatr Res. 2014; 57: 74–83.Google Scholar

Woodman, GF. A brief introduction to the use of event-related potentials in studies of perception and attention. Atten Percept Psychophys [Internet]. 2010 November; 72(8): 2031–2046. Available from: www.ncbi.nlm.nih.gov/pubmed/21097848CrossRefGoogle Scholar

Vandoolaeghe, E, van Hunsel, F, Nuyten, D, Maes, M. Auditory event related potentials in major depression: prolonged P300 latency and increased P200 amplitude. J Affect Disord. 1998 March; 48(2–3): 105–113.Google Scholar

Pfefferbaum, A, Wenegrat, BG, Ford, JM, Roth, WT, Kopell, BS. Clinical application of the P3 component of event-related potentials. II. Dementia, depression and schizophrenia. Electroencephalogr Clin Neurophysiol. 1984 April; 59(2): 104–124.Google Scholar

Diner, BC, Holcomb, PJ, Dykman, RA. P300 in major depressive disorder. Psychiatry Res. 1985 July; 15(3): 175–184.Google Scholar