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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (3): 221-228    DOI: 10.31083/JIN-170054
Research article Previous articles | Next articles
Neurobiological parameters in quantitative prediction of treatment outcome in schizophrenic patients
Andrey F. Iznak1, *(), Ekaterina V. Iznak1, Tatiana P. Klyushnik2, Georgy M. Kobel'kov3, Elena V. Damjanovich1, 4, Igor V. Oleichik5, Lilia I. Abramova5
1 Laboratory of Neurophysiology, Mental Health Research Center, Moscow, Russia
2 Laboratory of Neuroimmunology, Mental Health Research Center, Moscow, Russia
3 Department of Computational Mathematics, Faculty of Mechanics and Mathematics, M.V. Lomonosov Moscow State University, Moscow, Russia
4 Department of Brain Research, Research Center of Neurology, Moscow, Russia
5 Department of Endogenous Mental Disorders and Affective Conditions, Mental Health Research Center, Moscow, Russia
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Abstract  

The aim of this study was to reveal the set of neurobiological parameters informative for individual quantitative prediction of therapeutic response in schizophrenic subjects. Correlation and regression analyses of quantitative Positive And Negative Syndromes Scale clinical scores, together with background electroencephalographic spectral power values and four immunological parameters: enzymatic activity of leukocyte elastase and of alpha-1 proteinase inhibitor, as well as serum levels of autoantibodies to common myelin protein and to nerve growth factor, were performed for 50 female subjects with hallucinatory-delusional disorders such as attack-like paranoid schizophrenia. Background neurobiological data obtained before the beginning of a syndrome based treatment course were matched with Positive And Negative Syndromes Scale clinical scores of the same subjects after a treatment course to the stage of establishment of remission. The multiple linear regression equations were created which were described by only three or four (from an initial 80) background electroencephalographic parameters and one of four immunological parameters. These mathematical models allowed prediction of 65-76% of Positive and Negative Syndromes Scale score variance after a treatment course. The data obtained may be useful for elaboration of methods for individual quantitative prediction of treatment outcome for schizophrenic subjects.

Key words:  Quantitative electroencephalography      immunological parameters      paranoid schizophrenia      hallucinatory-delusional disorders      mathematical modeling      prediction of treatment outcome     
Submitted:  14 August 2017      Accepted:  13 October 2017      Published:  15 August 2018     
*Corresponding Author(s):  Andrey F. Iznak     E-mail:  iznak@inbox.ru

Cite this article: 

Andrey F. Iznak, Ekaterina V. Iznak, Tatiana P. Klyushnik, Georgy M. Kobel'kov, Elena V. Damjanovich, Igor V. Oleichik, Lilia I. Abramova. Neurobiological parameters in quantitative prediction of treatment outcome in schizophrenic patients. Journal of Integrative Neuroscience, 2018, 17(3): 221-228.

URL: 

https://jin.imrpress.com/EN/10.31083/JIN-170054     OR     https://jin.imrpress.com/EN/Y2018/V17/I3/221

Fig. 1.  Topographic maps of distribution of values of Spearman's correlation coefficients (r) between the PANSS-2-pos scores at the stage remission was established (visit two) and spectral power values of eight narrow frequency bands of background resting EEG (visit one) in subjects with paranoid schizophrenia and hallucinatory-delusional disorders. Color scale at right - in values of Spearman's r.

Fig. 2.  Topographic maps of distribution of values of Spearman's correlation coefficients (r) between the PANSS-2-neg scores at the stage remission was established (visit two) and spectral power values of eight narrow frequency bands of background resting EEG (visit one) in subjects with paranoid schizophrenia and hallucinatory-delusional disorders. Color scale at right - in values of Spearman's r.

Fig. 3.  Topographic maps of distribution of values of Spearman's correlation coefficients (r) between the PANSS-2-sum scores at the stage remission was established (visit two) and spectral power values in eight narrow frequency bands of background resting EEG (visit one) in subjects with paranoid schizophrenia and hallucinatory-delusional disorders Color scale at right - in values of Spearman's r.

Table 1  Correlation coefficients between outcome clinical assessments (visit two) and background immunological parameters (visit one) in subjects of the learning sample (n= 50) .
Outcome PANSS scores LE α 1-PI AAT_CMP AAT_GF
PANSS-2 positive -0.155 0.233 0.354* 0.113
PANSS-2 negative -0.227 0.049 0.201 0.146
PANSS-2 sum -0.212 0.073 0.195 0.365*
Table 2  An example of testing of mathematical models for quantitative prediction of clinical outcome. Subject I., Female, age 30 (control group). D-s: attack-like paranoid schizophrenia with hallucinatory-delusional disorders (F20.0 by ICD-10; 295.3 by DSM-IV-R).
PANSS scores Actual score after treatment Predicted score after treatment Deviation predicted vs. Actual scores Permitted deviation (p < 0.001)
PANSS-2 positive 16 14 12.50% ± 38%
PANSS-2 negative 19 17.5 8% ± 24%
PANSS-2 sum 76 71.4 6% ± 36%
Table 3  An example of testing of mathematical models for quantitative prediction of clinical outcome. Subject B., Female, age 32 (control group). D-s: attack-like paranoid schizophrenia with hallucinatory-delusional disorders (F20.0 by ICD-10; 295.3 by DSM-IV-R).
PANSS scores Actual score after treatment Predicted score after treatment Deviation predicted vs. Actual scores Permitted deviation (p < 0.001)
PANSS-2 positive 10 11 10% ± 38%
PANSS-2 negative 16 17 5% ± 24%
PANSS-2 sum 60 74 24% ± 36%
[1] Campbell M, Young PI, Bateman DN, Smith JM, Thomas SH ( 1999) The use of atypical antipsychotics in the management of schizophrenia. British Journal of Clinical Pharmacology 47( 1), 13-22.
doi: 10.1046/j.1365-2125.1999.00849.x pmid: 2014208
[2] Beaumont G ( 2000) Antipsychotics-the future of schizophrenia treatment. Current Medical Research and Opinion 16( 1), 37-42.
doi: 10.1185/0300799009117006 pmid: 16422033
[3] Cook IA ( 2008) Biomarkers in psychiatry: potentials, pitfalls, and pragmatics. Primary Psychiatry 15( 3), 54.
[4] Leuchter AF, Cook IA, Marangell LB, Gilmer WS, Burgoyne KS, Howland RH, Trivedi MH, Zisook S, Jain R, McCracken JT ( 2009) Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: results of the BRITE-MD study. Psychiatry Research 169( 2), 124-131.
doi: 10.1016/j.psychres.2009.06.004 pmid: 19712979
[5] Iosifescu DV ( 2011) Electroencephalography-derived biomarkers of antidepressant response. Harvard Review of Psychiatry 19( 3), 144-154.
doi: 10.3109/10673229.2011.586549
[6] Baskaran A, Milev R, McIntyre RS ( 2012) The neurobiology of the EEG biomarker as a predictor of treatment response in depression. Neuropharmacology 63( 4), 507-513.
doi: 10.1016/j.neuropharm.2012.04.021 pmid: 22569197
[7] Ulrich G, Renfordt E, Frick K ( 1986) The topographical distribution of alpha-activity in the resting EEG of endogenous-depressive in-patients with and without clinical response to pharmacotherapy. Pharmacopsychiatry 19( 04), 272-273.
doi: 10.1055/s-2007-1017230
[8] Ulrich G, Renfordt E, Zeller G, Frick K ( 1984) Interrelation between changes in the EEG and psychopathology under pharmacotherapy for endogenous depression. Pharmacopsychiatry 17( 06), 178-183.
doi: 10.1055/s-2007-1017433
[9] Knott VJ, Telner JI, Lapierre YD, Browne M, Horn ER ( 1996) Quantitative EEG in the prediction of antidepressant response to imipramine. Journal of Affective Disorders 39( 3), 175-184.
doi: 10.1016/0165-0327(96)00003-1
[10] Bruder GE, Sedoruk JP, Stewart JW, McGrath PJ, Quitkin FM, Tenke CE ( 2008) Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: preand post-treatment findings. Biological Psychiatry 63( 12), 1171-1177.
doi: 10.1016/j.biopsych.2007.10.009 pmid: 18061147
[11] Knott V, Mahoney C, Kennedy S, Evans K ( 2000) Pre-treatment EEG and it’s relationship to depression severity and paroxetine treatment outcome. Pharmacopsychiatry 33( 06), 201-205.
doi: 10.1055/s-2000-8356 pmid: 11147926
[12] Iosifescu DV, Greenwald S, Devlin P, Mischoulon D, Denninger JW, Alpert JE, Fava M ( 2009) Frontal EEG predictors of treatment outcome in major depressive disorder. European Neuropsychopharmacology 19( 11), 772-777.
doi: 10.1016/j.euroneuro.2009.06.001 pmid: 19574030
[13] Debener S, Beauducel A, Nessler D, Brocke B, Heilemann H, Kayser J ( 2000) Is resting anterior EEG alpha asymmetry a trait marker for depression? Neuropsychobiology 41( 1), 31-37.
doi: 10.1159/000026630
[14] Bruder GE, Stewart JW, Tenke CE, McGrath PJ, Leite P, Bhattacharya N, Quitkin FM ( 2001) Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant. Biological psychiatry 49( 5), 416-425.
doi: 10.1016/S0006-3223(00)01016-7 pmid: 11274653
[15] Suffin SC, Emory WH ( 1995) Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clinical Electroencephalography 26( 2), 76-83.
doi: 10.1177/155005949502600204 pmid: 7781194
[16] Tenke CE, Kayser J, Manna CG, Fekri S, Kroppmann CJ, Schaller JD, Alschuler DM, Stewart JW, McGrath PJ, Bruder GE ( 2011) Current source density measures of electroencephalographic alpha predict antidepressant treatment response. Biological psychiatry 70( 4), 388-394.
doi: 10.1016/j.biopsych.2011.02.016
[17] Bruder GE, Tenke CE, Stewart JW, Towey JP, Leite P, Voglmaier M, Quitkin FM ( 1995) Brain event-related potentials to complex tones in depressed patients: Relations to perceptual asymmetry and clinical features. Psychophysiology 32( 4), 373-381.
doi: 10.1111/j.1469-8986.1995.tb01220.x pmid: 7652114
[18] Kalayam B, Alexopoulos GS ( 1999) Prefrontal dysfunction and treatment response in geriatric depression. Archives of General Psychiatry 56( 8), 713-718.
doi: 10.1001/archpsyc.56.8.713 pmid: 10435605
[19] Leuchter AF, Cook IA, Lufkin RB, Dunkin J, Newton TF, Cummings JL, Mackey JK, Walter DO ( 1994) Cordance: a new method for assessment of cerebral perfusion and metabolism using quantitative electroencephalography. Neuroimage 1( 3), 208-219.
doi: 10.1006/nimg.1994.1006 pmid: 9343572
[20] Cook IA, Leuchter AF, Morgan M, Witte E, Stubbeman WF, Abrams M, Rosenberg S, Uijtdehaage SH ( 2002) Early changes in prefrontal activity characterize clinical responders to antidepressants. Neuropsychopharmacology 27( 1), 120-131.
doi: 10.1016/S0893-133X(02)00294-4 pmid: 12062912
[21] Bares M, Brunovsky M, Kopecek M, Novak T, Stopkova P, Kozeny J, Sos P, Krajca V, H¨oschl C ( 2008) Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder. European Psychiatry 23( 5), 350-355.
doi: 10.1016/j.eurpsy.2008.03.001 pmid: 18450430
[22] Itil T, Le Bars P, Eralp E ( 1994) Quantitative EEG as biological marker. Neuropsychopharmacology 10( 4), 310-315.
[23] Czobor P, Volavka J ( 1991) Pretreatment EEG predicts short-term response to haloperidol treatment. Biological Psychiatry 30( 9), 927-942.
doi: 10.1016/0006-3223(91)90006-8 pmid: 1747438
[24] Galderisi S, Maj M, Mucci A, Bucci P, Kemali D ( 1994) QEEG alpha1 changes after a single dose of high-potency neuroleptics as a predictor of short-term response to treatment in schizophrenic patients. Biological Psychiatry 35( 6), 367-374.
doi: 10.1016/0006-3223(94)90002-7 pmid: 8018782
[25] Mel’nikova TS, Lapin IA, Sarkisian VV ( 2009) Informativity of use of coherence analysis in psychiatry. Funktzional ’Naya Diagnostika 1, 88-93.
[26] Iznak A, Iznak E, Oleichik I, Abramova L, Sorokin S, Stoliarov S ( 2014) EEG-correlates of frontal dysfunction as predictors of relative pharmacoresistance in treatment of endogenous affective disorders. Zhurnal Nevrologii I Psikhiatrii Imeni SS Korsakova 114( 12), 54-59.
doi: 10.17116/jnevro201411412154-59
[27] Iznak A, Iznak E, Yakovleva O, Safarova T, Kornilov V ( 2013) Neurophysiological measures of treatment efficacy in late-onset depression. Neuroscience and Behavioral Physiology 43( 9), 1113-1120.
doi: 10.1007/s11055-013-9858-1
[28] Iznak A, Tiganov A, Iznak E, Sorokin S ( 2013) EEG correlates and possible predictors of the efficacy of the treatment of endogenous depression. Human Physiology 39( 4), 378-385.
doi: 10.1134/S0362119713040063
[29] Mitrofanov AA ( 2005) Computerized system for analysis and topographic mapping of brain electrical activity with neurometric EEG data bank (description and use). User’s Manual, Moscow. ( In Russian).
[30] Olie JP, Macher JP , Costa e Silva JA( 2004) Neuroplasticity: A New Approach to the Pathophysiology of Depression. London, Science Press.
[31] MÜller N, Schwarz M ( 2006) Schizophrenia as an inflammationmediated dysbalance of glutamatergic neurotransmission. Neurotoxicity Research 10( 2), 131-148.
doi: 10.1007/BF03033242 pmid: 17062375
[32] Maes M, Yirmyia R, Noraberg J, Brene S, Hibbeln J, Perini G, Kubera M, Bob P, Lerer B, Maj M ( 2009) The inflammatory & neurodegenerative (I&ND) hypothesis of depression: leads for future research and new drug developments in depression. Metabolic Brain Disease 24( 1), 27-53.
doi: 10.1007/s11011-008-9118-1
[33] Kliushnik T, Zozulia S, Androsova L, Sarmanova Z, Otman I, Dupin A, Panteleeva G, Oleichik I, Abramova L, Stoliarov S ( 2014) Immunological monitoring of endogenous attack-like psychoses. Zhurnal Nevrologii I Psikhiatrii Imeni SS Korsakova 114( 2), 37-41.
doi: 10.17116/jnevro201411411237-39 pmid: 25591653
[34] Royall DR, Al-Rubaye S, Bishnoi R, Palmer RF ( 2017) Serum proteins mediate depression’s association with dementia. Plos One 12( 6), e0175790.
doi: 10.1371/journal.pone.0175790 pmid: 5464526
[35] Iznak A, Iznak E, Klyushnik T, Oleichik I, Abramova L, Kobel’kov G, Lozhnikov M ( 2016) Regression models of interrelationships between clinical and neurobiological parameters in treatment of manic-delusional conditions in attack-like schizophrenia. Zhurnal Nevrologii I Psikhiatrii Imeni SS Korsakova 116( 3), 33-38.
doi: 10.17116/jnevro20161163133-38 pmid: 27070470
[36] Kobel’kov GM, Kornev AA, Ol’shanskiy MA, Chizhonkov EV ( 2009) Some actual problems of mathematicalmodeling. Contemporary Problems of Mathematics and Mechanics, Ser 2 Applied Mathematics 5, 121-154.
[37] Kay SR, Fiszbein A, Opler LA ( 1987) The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13( 2), 261-276.
doi: 10.1093/schbul/13.2.261
[38] Lequin RM ( 2005) Enzyme immunoassay (EIA)/enzyme-linked immunosorbent assay (ELISA). Clinical Chemistry 51( 12), 2415-2418.
doi: 10.1373/clinchem.2005.051532 pmid: 16179424
[39] Klyushnik TP, Zozulya SA, Androsova LV , et al. ( 2014) Laboratory Diagnostics in Monitoring of Patients with Endogenous Psychoses (“Neuro-Immuno-Test”). Moscow, MIA Publishers. ( In Russian).
[40] Gruzelier JH ( 1999) Functional neuropsychophysiological asymmetry in schizophrenia: a review and reorientation. Schizophrenia Bulletin 25( 1), 91-120.
doi: 10.1093/oxfordjournals.schbul.a033370 pmid: 10098916
[41] Boutros NN, Mucci A, Vignapiano A, Galderisi S ( 2014) Electrophysiological aberrations associated with negative symptoms in schizophrenia. Current Topics in Behavioral Neurosciences 21, 129-156.
doi: 10.1007/7854_2011_121
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