- Email: [email protected]
Development of neuromarkers for mental disorders
IOP 2016
vigil of the hypnosis state. Variables such as sex, level of hypnotic depth, Relative Power, Absolute Power, Total Absolute Power, Median Frequency and Total Median Frequency were analyzed. They were used as summary measures the percentage, the arithmetic mean and the standard deviation. In addition, it was used as hypothesis tests: the t' student test to measure differences and the statistician False Discovery Rate, through the Quantitative Analysis Module of Medicid 5 (Neuronic). To validate the statistical information it was used as technique multivariate analysis of variance MANOVA. For all the hypothesis tests, it was considered a level of statistical significance of 0.05. The most outstanding results show that the Relative Power of the Theta band ascended in the initial and half levels of the hypnosis; the Fp2 and F4 derivations, displayed a greater number of modified measures. The conclusion rests that quantitative analysis of EEG broadband, allows distinguishing the vigil from the hypnosis state in young healthy people. doi:10.1016/j.ijpsycho.2016.07.317
330 Machine learning based framework for EEG/ERP analysis Rober Boshra, Kyle Ruiter, James Reilly, John Connolly McMaster University, Hamilton, Canada Introduction/Background: Event Related Potential (ERP) analysis of Electroencephalography (EEG) data has been widely used in research on language, cognition, and pathology. The high dimensionality of a typical EEG/ERP dataset makes it a time-consuming prospect to properly analyze, explore, and validate knowledge without a particular restricted hypothesis. Methods: This study proposes an automated empirical greedy approach to the analysis process to datamine an EEG dataset for the location, robustness, and latency of ERPs, if any, present in a given dataset. We utilize Support Vector Machines (SVM), a well established machine learning model, with a feature selection algorithm named minimum redundancy maximum relevancy (mRMR) (Peng et al., 2005), on top of a preprocessing pipeline that produces a large bag of features including auto/cross power spectral densities, skewness, kurtosis, and electrode amplitudes. A hybrid of monte-carlo bootstrapping, crossvalidation, and permutation tests is used to ensure the reproducibility of results. Results: This method has been tested and validated on three different datasets with different ERPs (N100, Mismatch Negativity (MMN), Phonological Mapping Negativity (PMN), and P300). Results show statistical significance in the identification of all ERPs in their respective experimental conditions, latency, and location. Limitations: As machine learning approaches were used in this study, a relatively large number of trials is needed to extract knowledge from a given dataset. In the case of a small dataset, type II errors are prevalent due to the conservative nature of the algorithm. Furthermore, this framework has been built to compare only two conditions. Extending it for more classes was outside the scope of this study. Conclusion: This study introduces an easy to use framework for EEG/ERP dataset analysis. The algorithms used serve to reduce researcher bias, time spent during analysis, and provide statistically sound results that are agnostic to dataset specifications including the ERPs in question. doi:10.1016/j.ijpsycho.2016.07.318
105
335 Development of neuromarkers for mental disorders Sergey A. Evdokimov, Yury I. Polyakov, Valery A. Ponomarev, Jury D. Kropotov N.P. Bekhtereva Institute of the Human Brain, Russian Academy of Sciences, Laboratory of neurobiology of action programming, St.-Petersburg, Russia It was shown in many studies that the mechanisms of central control are divided into the processes of engaging into the necessary action (initiation, selection of sensory-motor cognitive acts) and the suppression of the unnecessary actions. Using the parameters of ERPs, EEG spectra and independent components to determine parameters disturbed in depression and schizophrenia showed differences between the groups of patients and healthy subjects with high level of statistical significance and changes in neurophysiological parameters and allows determining specificity of its changes (Kropotov et al., 2013, Evdokimov et al., 2014). We investigated the possibility of classify the data of patients with different psychiatric disorders on the base of physiological measures of the brain activity. The event-related potentials registered during the performance of GO/NOGO task and EEG spectra were calculated for the age-matched groups of adult patients with schizophrenia (two groups: 32 subjects, mean age = 26.5, SD = 7.1, 27 male and 19 subjects, mean age = 40.5, SD = 6.1, 12 male), major depression (19 subjects, mean age = 42.2, SD = 13.7, 9 male) and obsessivecompulsive disorder (13 subjects, mean age = 27.6, SD = 10.4, 8 male). Separation of independent components for the event-related potentials was performed by applying Independent Component Analysis method. Picked out components were used in the method of discriminant analysis to classify the data according to the clinical diagnosis. The same method is used to compare the groups using the parameters of EEG spectra (power in theta, alpha, beta-1 and beta-2 frequency bands). Results: According to the data of the discriminant, analysis accuracy was more than 85% for the diagnosis by parameters of independent components of ERPs for patients with obsessive-compulsive disorder, depressive disorder and schizophrenia. In opposite, we found the specificity and sensitivity between 75% and 85% for the parameters of EEG spectra. The result is helpful for the existing ideas about the organization of the brain system for behavior control and impairment of its functioning in mental illness. In addition, it will complement for the knowledge of the neurophysiological bases for developing schizophrenia, obsessive-compulsive and depressive disorders. Conclusion: The discriminant analysis of components for the event-related potentials and the power of EEG spectra shows greater significance of categorization for independent components of ERPs. The Russian Science Foundation (project no. 16-15-10213) supported this study. doi:10.1016/j.ijpsycho.2016.07.319
342 Changes in Event Related Potentials after exposure therapy for spider phobic individuals Jens Bernhardsson, Anna Bjarta, Orjan Sundin Mid Sweden University, Ostersund, Sweden The present study was conducted in order to investigate treatment effects in spider phobic individuals on EEG and eye movements. A
vigil of the hypnosis state. Variables such as sex, level of hypnotic depth, Relative Power, Absolute Power, Total Absolute Power, Median Frequency and Total Median Frequency were analyzed. They were used as summary measures the percentage, the arithmetic mean and the standard deviation. In addition, it was used as hypothesis tests: the t' student test to measure differences and the statistician False Discovery Rate, through the Quantitative Analysis Module of Medicid 5 (Neuronic). To validate the statistical information it was used as technique multivariate analysis of variance MANOVA. For all the hypothesis tests, it was considered a level of statistical significance of 0.05. The most outstanding results show that the Relative Power of the Theta band ascended in the initial and half levels of the hypnosis; the Fp2 and F4 derivations, displayed a greater number of modified measures. The conclusion rests that quantitative analysis of EEG broadband, allows distinguishing the vigil from the hypnosis state in young healthy people. doi:10.1016/j.ijpsycho.2016.07.317
330 Machine learning based framework for EEG/ERP analysis Rober Boshra, Kyle Ruiter, James Reilly, John Connolly McMaster University, Hamilton, Canada Introduction/Background: Event Related Potential (ERP) analysis of Electroencephalography (EEG) data has been widely used in research on language, cognition, and pathology. The high dimensionality of a typical EEG/ERP dataset makes it a time-consuming prospect to properly analyze, explore, and validate knowledge without a particular restricted hypothesis. Methods: This study proposes an automated empirical greedy approach to the analysis process to datamine an EEG dataset for the location, robustness, and latency of ERPs, if any, present in a given dataset. We utilize Support Vector Machines (SVM), a well established machine learning model, with a feature selection algorithm named minimum redundancy maximum relevancy (mRMR) (Peng et al., 2005), on top of a preprocessing pipeline that produces a large bag of features including auto/cross power spectral densities, skewness, kurtosis, and electrode amplitudes. A hybrid of monte-carlo bootstrapping, crossvalidation, and permutation tests is used to ensure the reproducibility of results. Results: This method has been tested and validated on three different datasets with different ERPs (N100, Mismatch Negativity (MMN), Phonological Mapping Negativity (PMN), and P300). Results show statistical significance in the identification of all ERPs in their respective experimental conditions, latency, and location. Limitations: As machine learning approaches were used in this study, a relatively large number of trials is needed to extract knowledge from a given dataset. In the case of a small dataset, type II errors are prevalent due to the conservative nature of the algorithm. Furthermore, this framework has been built to compare only two conditions. Extending it for more classes was outside the scope of this study. Conclusion: This study introduces an easy to use framework for EEG/ERP dataset analysis. The algorithms used serve to reduce researcher bias, time spent during analysis, and provide statistically sound results that are agnostic to dataset specifications including the ERPs in question. doi:10.1016/j.ijpsycho.2016.07.318
105
335 Development of neuromarkers for mental disorders Sergey A. Evdokimov, Yury I. Polyakov, Valery A. Ponomarev, Jury D. Kropotov N.P. Bekhtereva Institute of the Human Brain, Russian Academy of Sciences, Laboratory of neurobiology of action programming, St.-Petersburg, Russia It was shown in many studies that the mechanisms of central control are divided into the processes of engaging into the necessary action (initiation, selection of sensory-motor cognitive acts) and the suppression of the unnecessary actions. Using the parameters of ERPs, EEG spectra and independent components to determine parameters disturbed in depression and schizophrenia showed differences between the groups of patients and healthy subjects with high level of statistical significance and changes in neurophysiological parameters and allows determining specificity of its changes (Kropotov et al., 2013, Evdokimov et al., 2014). We investigated the possibility of classify the data of patients with different psychiatric disorders on the base of physiological measures of the brain activity. The event-related potentials registered during the performance of GO/NOGO task and EEG spectra were calculated for the age-matched groups of adult patients with schizophrenia (two groups: 32 subjects, mean age = 26.5, SD = 7.1, 27 male and 19 subjects, mean age = 40.5, SD = 6.1, 12 male), major depression (19 subjects, mean age = 42.2, SD = 13.7, 9 male) and obsessivecompulsive disorder (13 subjects, mean age = 27.6, SD = 10.4, 8 male). Separation of independent components for the event-related potentials was performed by applying Independent Component Analysis method. Picked out components were used in the method of discriminant analysis to classify the data according to the clinical diagnosis. The same method is used to compare the groups using the parameters of EEG spectra (power in theta, alpha, beta-1 and beta-2 frequency bands). Results: According to the data of the discriminant, analysis accuracy was more than 85% for the diagnosis by parameters of independent components of ERPs for patients with obsessive-compulsive disorder, depressive disorder and schizophrenia. In opposite, we found the specificity and sensitivity between 75% and 85% for the parameters of EEG spectra. The result is helpful for the existing ideas about the organization of the brain system for behavior control and impairment of its functioning in mental illness. In addition, it will complement for the knowledge of the neurophysiological bases for developing schizophrenia, obsessive-compulsive and depressive disorders. Conclusion: The discriminant analysis of components for the event-related potentials and the power of EEG spectra shows greater significance of categorization for independent components of ERPs. The Russian Science Foundation (project no. 16-15-10213) supported this study. doi:10.1016/j.ijpsycho.2016.07.319
342 Changes in Event Related Potentials after exposure therapy for spider phobic individuals Jens Bernhardsson, Anna Bjarta, Orjan Sundin Mid Sweden University, Ostersund, Sweden The present study was conducted in order to investigate treatment effects in spider phobic individuals on EEG and eye movements. A