Affiliations: [a] Laboratory of Neurophysiology, Mental Health Research Center, Moscow, Russia | [b] Laboratory of Neuroimmunology, Mental Health Research Center, Moscow, Russia | [c] Department of Computational Mathematics, Faculty of Mechanics and Mathematics, M.V. Lomonosov Moscow State University, Moscow, Russia | [d] Department of Brain Research, Research Center of Neurology, Moscow, Russia | [e] Department of Endogenous Mental Disorders and Affective Conditions, Mental Health Research Center, Moscow, Russia
Corresponding author: Prof. Dr. Andrey F. Iznak, PhD, DSci, Laboratory of Neurophysiology, Mental Health Research Center, 34, Kashirskoye shosse, Moscow 115522, Russia. Tel.: +79167747043; E-mail: email@example.com.
Abstract: The aim of the study was to reveal the set of neurobiological parameters informative for individual quantitative prediction of therapeutic response in schizophrenic patients. Correlation and regression analyses of quantitative clinical scores (by Positive And Negative Syndromes Scale – PANSS), together with background EEG 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 in 50 patients (all females, aged 32.9±10.8 years) with hallucinatory-delusional disorders in the frames of attack-like paranoid schizophrenia. Background neurobiological data obtained before the beginning of syndrome based treatment course (at visit 1) were matched with PANSS clinical scores of the same patients after treatment course at the stage of remission establishment (at visit 2). The multiple linear regression equations were created which contained only 3 to 4 (from initial 80) background EEG parameters and one of four immunological parameters. These mathematical models allowed prediction from 65% to 76% of PANSS scores variance after treatment course (at visit 2). The data obtained may be used for elaboration of methods of individual quantitative prediction of treatment outcome in schizophrenic patients.