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2015 | 15 | 1 | 19-25
Article title

Predyktory odpowiedzi na leczenie przeciwdepresyjne w zapisie EEG i QEEG

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Title variants
EN
EEG and QEEG biomarkers as predictors of antidepressant treatment response
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EN PL
Abstracts
EN
The aim of this paper is to present current research on the role of EEG and QEEG (quantitative electroencephalogram) prognostic indicators in the prediction of antidepressant treatment outcomes. Depression is currently one of the most common psychiatric disorders with a lifetime prevalence of 7–25%. The choice of antidepressant is still based on a trial-and-error procedure, which is often unsuccessful. Therefore, further research is necessary to establish objective biomarkers and their combination in order to proceed to a faster and more efficacious treatment of depression. In quantitative EEG (QEEG) research, various pretreatment differences in QEEG measures have been reported to be associated with improved antidepressant treatment outcomes. In particular, the following correlations seem to be clinically significant: lower pretreatment theta power, decreased theta cordance 48 h to 2 weeks after start of medication, greater alpha power, increased theta in the rostral anterior cingulate, higher alpha power over the right hemisphere. In contrast increased theta and delta power have been associated with poor treatment response.
PL
Celem artykułu jest przedstawienie aktualnych badań dotyczących roli wskaźników prognostycznych EEG i QEEG (ilościowa elektroencefalografia) w przewidywaniu rezultatów leczenia przeciwdepresyjnego. Depresja należy do najczęstszych zaburzeń psychicznych; rozpowszechnienie w populacji to 7–25% w ciągu całego życia. Wybór leku przeciwdepresyjnego opiera się na metodzie prób i błędów, co często wiąże się z niepowodzeniami. Konieczne są dalsze badania zmierzające do ustalenia obiektywnych biomarkerów oraz ich kombinacji w celu przyspieszenia leczenia depresji i zwiększenia jego skuteczności. W badaniach z zastosowaniem QEEG różnice w pomiarach przed leczeniem korelowały z późniejszą poprawą kliniczną. W szczególności istotne klinicznie wydają się następujące zależności: niższa moc theta przed włączeniem leczenia, spadek wskaźnika kordancji theta w czasie od 48 godzin do 2 tygodni po rozpoczęciu farmakoterapii, wzrost mocy alfa, wzrost mocy theta nad zakrętem obręczy, większa moc alfa nad prawą półkulą. Z kolei wzrost mocy theta i delta wiąże się ze słabą odpowiedzią na leczenie przeciwdepresyjne.
Discipline
Year
Volume
15
Issue
1
Pages
19-25
Physical description
References
  • Arns MW, Sumich A: Prediction of treatment efficacy and side effects: major depression: a litterature summary. The Brain Resource Company, 2006. Available from: http://www.brainquiry.com/downloads/TreatmentEfficacy_Depression.pdf.
  • Bares M., Brunovsky M, Novak T et al.: The change of prefrontal QEEG theta cordance as a predictor of response to bupropion treatment in patients who had failed to respond to previous antidepressant treatments. Eur Neuropsychopharmacol 2010; 20: 459–466.
  • Bruder GE, Kayser J, Tenke CE: Event-related brain potentials in depression: clinical, cognitive and neurophysiologic implication. In: Luck SJ, Kappenman ES (eds.): The Oxford Handbook of Event-Related Potential Components. Oxford University Press, New York 2012: 563–592.
  • Bruder GE, Sedoruk JP, Stewart J et al.: Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: pre- and post-treatment findings. Biol Psychiatry 2008; 63: 1171–1177.
  • Bruder GE, Stewart JW, Mercier MA et al.: Outcome of cognitivebehavioral therapy for depression: relation to hemispheric dominance for verbal processing. J Abnorm Psychol 1997; 106: 138–144.
  • Bruder GE, Stewart JW, Tenke CE et al.: Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant. Biol Psychiatry 2001; 49: 416–425.
  • Cook IA, O’Hara R, Uijtdehaage SH et al.: Assessing the accuracy of topographic EEG mapping for determining local brain function. Electroencephalogr Clin Neurophysiol 1998; 107: 408–414.
  • Davidson RJ, Lewis DA, Alloy LB et al.: Neural and behavioral substrates of mood and mood regulation. Biol Psychiatry 2002; 52: 478–502.
  • Debener S, Beauducel A, Nessler D et al.: Is resting anterior alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology 2000; 41: 31–37.
  • Drago A, De Ronchi D, Serretti A: Pharmacogenetics of antidepressant response: an update. Hum Genomics 2009; 3: 257–274.
  • Drevets WC, Gadde KM, Krishnan KRR: Neuroimaging studies of mood disorders. In: Charney DS, Nestler EJ (eds.): Neurobiology of Mental Illness. Oxford University Press, New York 2004: 461–490.
  • Gotlib IH, Ranganath C, Rosenfeld P: Frontal EEG alpha asymmetry, depression, and cognitive functioning. Cogn Emot 1998; 12: 449–478.
  • Grin-Yatsenko VA, Baas I, Ponomarev VA et al.: Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clin Neurophysiol 2010; 121: 281–289.
  • Henriques JB, Davidson RJ: Left frontal hypoactivation in depression. J Abnorm Psychol 1991; 100: 535–545.
  • Hong CJ, Liou YJ, Tsai SJ: Effects of BDNF polymorphisms on brain function and behavior in health and disease. Brain Res Bull 2011; 86: 287–297.
  • Howland RH, Shutt LS, Berman SR et al.: The emerging use of technology for the treatment of depression and other neuropsychiatric disorders. Ann Clin Psychiatry 2011; 23: 48–62.
  • Hunter AM, Cook IA, Greenwald SD et al.: The antidepressant treatment response index and treatment outcomes in a placebo-controlled trial of fluoxetine. J Clin Neurophysiol 2011; 28: 478–482.
  • Hunter AM, Leuchter AF, Cook IA et al.: Brain functional changes (QEEG cordance) and worsening suicidal ideation and mood symptoms during antidepressant treatment. Acta Psychiatr Scand 2010; 122: 461–469.
  • Hunter AM, Leuchter AF, Morgan ML et al.: Neurophysiologic correlates of side effects in normal subjects randomized to venlafaxine or placebo. Neuropsychopharmacology 2005; 30: 792–799.
  • Iosifescu DV: Prediction of response to antidepressants: is quantitative EEG (QEEG) an alternative? CNS Neurosci Ther 2008; 14: 263–265.
  • Iosifescu DV, Greenwald S, Devlin P et al.: Frontal EEG predictors of treatment outcome in major depressive disorder. Eur Neuropsychopharmacol 2009; 19: 772–777.
  • Knott VJ, Telner JI, Lapierre YD et al.: Quantitative EEG in the prediction of antidepressant response to imipramine. J Affect Disord 1996; 39: 175–184.
  • Kovacs D, Gonda X, Petschner P et al.: Antidepressant treatment response is modulated by genetic and environmental factors and their interactions. Ann Gen Psychiatry 2014; 13: 17.
  • Labermaier CH, Masana M, Müller M: Biomarkers predicting antidepressant treatment response: how can we advance the field? Dis Markers 2013; 35: 23–31.
  • Leuchter AF, Cook IA, Lufkin RB et al.: Cordance: a new method for assessment of cerebral perfusion and metabolism using quantitative electroencephalography. Neuroimage 1994; 1: 208–219.
  • Leuchter AF, Cook IA, Uijtdehaage SH et al.: Brain structure and function and the outcomes of treatment for depression. J Clin Psychiatry 1997; 58 Suppl 16: 22–31.
  • Mayberg HS, Brannan SK, Mahurin RK et al.: Cingulate function in depression: a potential predictor of treatment response. Neuroreport 1997; 8: 1057–1061.
  • Milak MS, Keilp J, Parsey RV et al.: Regional brain metabolic correlates of self-reported depression severity contrasted with clinical ratings. J Affect Disord 2010; 126: 113–124.
  • Mulert C, Juckel G, Brunnmeier M et al.: Rostral anterior cingulate cortex activity in the theta band predicts response to antidepressive medication. Clin EEG Neurosci 2007; 38: 78–81.
  • Pizzagalli DA: Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 2011; 36: 183–206.
  • Pollock VE, Schneider LS: Topographic quantitative EEG in elderly subjects with major depression. Psychophysiology 1990; 27: 438–444.
  • Prichep LS, Mas F, Hollander E et al.: Quantitative electroencephalographic subtyping of obsessive-compulsive disorder. Psychiatry Res 1993; 50: 25–32.
  • Rush AJ, Kraemer HC, Sackeim HA et al.: Report by the ACNP Task Force on response and remission in major depressive disorder. Neuropsychopharmacology 2006; 31: 1841–1853.
  • Spronk D, Arns M, Barnet KJ et al.: An investigation of EEG, genetic and cognitive markers of treatment response to antidepressant medication in patients with major depressive disorder: a pilot study. J Affect Disord 2011; 128: 41–48.
  • Stewart JL, Bismark AW, Towers DN et al.: Resting frontal EEG asymmetry as an endophenotype for depression risk: sex-specific patterns of frontal brain asymmetry. J Abnorm Psychol 2010; 119: 502–512.
  • Suffin SC, Emory WH: Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clin Elelectroencephalogr 1995; 26: 76–83.
  • Tenke CE, Kayser J, Manna CG et al.: Current source density measures of electroencephalographic alpha predict antidepressant treatment response. Biol Psychiatry 2011; 70: 388–394.
  • Ulrich G, Renfordt E, Frick K: The topographical distribution of alpha-activity in the resting EEG of endogenous-depressive inpatients with and without clinical response to pharmacotherapy. Pharmacopsychiatry 1986; 19: 272–273.
Document Type
review
Publication order reference
YADDA identifier
bwmeta1.element.psjd-50abb53f-f5ee-4876-b0c4-51f7476ac3ee
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