Nevzat Tarhan

Simultaneous EEG/fMRI Analysis of the Resonance Phenomena in Steady-State Visual Evoked Responses:

The stability of the steady-state visual evoked potentials (SSVEPs) across trials and subjects makes them a suitable tool for the investigation of the visual system. The reproducible pattern of the frequency characteristics of SSVEPs shows a global amplitude maximum around 10 Hz and additional local maxima around 20 and 40 Hz, which have been argued to represent resonant behavior of damped neuronal oscillators. Simultaneous electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) measurement allows testing of the resonance hypothesis about the frequency-selective increases in SSVEP amplitudes in human subjects, because the total synaptic activity that is represented in the fMRI-Blood Oxygen Level Dependent (fMRI-BOLD) response would not increase but get synchronized at the resonance frequency. For this purpose, 40 healthy volunteers were visually stimulated with flickering light at systematically varying frequencies between 6 and 46 Hz, and the correlations between SSVEP amplitudes and the BOLD responses were computed. The SSVEP frequency characteristics of all subjects showed 3 frequency ranges with an amplitude maximum in each of them, which roughly correspond to alpha, beta and gamma bands of the EEG. The correlation maps between BOLD responses and SSVEP amplitude changes across the different stimulation frequencies within each frequency band showed no significant correlation in the alpha range, while significant correlations were obtained in the primary visual area for the beta and gamma bands. This non-linear relationship between the surface recorded SSVEP amplitudes and the BOLD responses of the visual cortex at stimulation frequencies around the alpha band supports the view that a resonance at the tuning frequency of the thalamo-cortical alpha oscillator in the visual system is responsible for the global amplitude maximum of the SSVEP around 10 Hz. Information gained from the SSVEP/fMRI analyses in the present study might be extrapolated to the EEG/fMRI analysis of the transient event-related potentials (ERPs) in terms of expecting more reliable and consistent correlations between EEG and fMRI responses, when the analyses are carried out on evoked or induced oscillations (spectral perturbations) in separate frequency bands instead of the time-domain ERP peaks.

Transcranial magnetic stimulation for treating depression in elderly patients

Purpose The aim of the study reported here was to examine the safety and effectiveness of high-frequency repetitive transcranial magnetic stimulation (rTMS) in elderly patients with depression. Patients and methods Sixty-five depressed elderly patients received rTMS over their left prefrontal cortex for 6 days per week, from Monday to Saturday, for 3 weeks. The rTMS intensity was set at 100% of the motor threshold and 25 Hz stimulation with a duration of 2 seconds and was delivered 20 times at 30-second intervals. A full course comprised an average of 1000 magnetic pulses. Depression was rated using the Hamilton Depression Rating Scale (HAMD) before and after treatment. Response was defined as a 50% reduction in HAMD score. Patients with HAMD scores < 8 were considered to be in remission. Results The mean HAMD score for the study group decreased from 21.94 ± 5.12 before treatment to 11.28 ± 4.56 after rTMS (P < 0.001). Following the treatment period, 58.46% of the study group demonstrated significant mood improvement, as indexed by a reduction of more than 50% on the HAMD score. Nineteen of these 38 patients attained remission (HAMD score < 8), while 41.54% of all study patients achieved a partial response. None of the patients had a worsened HAMD score at the end of the treatment. Treatment was generally well tolerated and no serious adverse effects were reported. Conclusion In this study, rTMS was found to be a safe, well-tolerated treatment, and a useful adjunctive treatment to medications in elderly treatment-resistant depressed patients. This study contributes to the existing evidence on the antidepressant effect of rTMS in the treatment of depression in patients over 60 years of age.

Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

Objective The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). Methods The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. Results The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. Conclusion Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

Efficacy of High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression

We examined the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in 419 patients with treatment-resistant depression. The patients received daily sessions of rTMS over the left prefrontal cortex as an adjuvant to pharmacotherapy. The rTMS intensity was set at 100% of the motor threshold and 25 Hz stimulation, with train duration of 2 seconds delivered at 30-second intervals. A full course comprised 1000 magnetic pulses. Depression was rated using the Hamilton Depression Rating Scale (HAMD) before and after treatment. Response was defined as a 50% reduction in the HAMD score. Patients with HAMD scores of less than 8 were considered to be in remission. The mean HAMD score for the study group decreased from 22.59 ± 5.92 to 10.50 ± 5.83 (P < .001). After the treatment period, 268 (64%) out of 419 patients demonstrated significant mood improvements, as indexed by a reduction of more than 50% on the HAMD score. In addition, 140 patients (33.4%) attained remission (HAMD score <8); and 11 patients achieved a partial response. Treatment was generally well tolerated, and no serious adverse effects were reported. In conclusion, high-frequency (25 Hz) rTMS was well tolerated and found to be statistically and clinically effective in patients with treatment-resistant depression. This study contributed to the existing evidence of the antidepressant effect of rTMS in the treatment of depression.

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

Objective Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. Methods Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects. Results BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset. Conclusion ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.

RETRACTED: Are uric acid plasma levels different between unipolar depression with and without adult attention deficit hyperactivity disorder?

BACKGROUND The aim of our study is to compare uric acid plasma levels in patients with unipolar depression between those with Attention deficit hyperactivity disorder (ADHD) comorbidity and those without. Our hypothesis is that uric acid plasma levels may be higher in unipolar depressive patients with adult ADHD than without ADHD. METHODS Sixty four patients diagnosed with MDD were investigated, among which 28 patients had been diagnosed with ADHD according to DSM5. 28 patients were ADHD. 36 patients were diagnosed as not having ADHD. One of the criteria was including cases that had not started using medication for the current depressive episode. The control group (HC) consisted of 43 healthy staff members from our hospital who had no prior psychiatric admission or treatment history and matched with the patient group in terms of age and gender. Blood samples were obtained, and plasma uric acid levels were recorded in mg/dl after being rotated for 15min in a centrifuge with 3000 rotations and kept at -80°C. RESULTS Uric acid plasma levels 5.1±1.6 in unipolar depression and ADHD group, 4.6±1.8 in unipolar depression group. Uric acid plasma levels were higher in the comorbid unipolar depression and ADHD group than in the unipolar depression and healthy control (HC) groups (F= 4.367, p= 0.037). There was no correlation between ADHD (predominantly inattentive type) and uric acid plasma levels (p>0.05). LIMITATIONS The limitation of this study is the small number of sample and one of the criteria was including cases that had not started using medication for the current depressive episode. CONCLUSION The identification of a different etiologic process of biological markers may lead to a better understanding of the physiological mechanisms involved in drive and impulsivity and may suggest different potential targets for therapeutic intervention.

Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression.

Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in the brain, and is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The effect of rTMS treatment on the brain can be evaluated by cordance, a quantitative electroencephalography (QEEG) method that extracts information from absolute and relative power of EEG spectra. In this study, to analyze brain functional changes, pre- and post-rTMS, QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7, and F8) in 2 slow bands (delta and theta) for 55 MDD subjects. To examine brain changes, cordance scores were determined, using repeated-measures analysis of variance (ANOVA). High-frequency rTMS was associated with cordance decrease in left frontal and right prefrontal regions in both delta and theta for nonresponders; it was associated with cordance increase in all right and left frontal electrodes, except F8, for responders.

Reward Processing Deficits During a Spatial Attention Task in Patients With ADHD: An fMRI Study:

Objective: In this study, we aimed to explore how cues signaling rewards and feedbacks about rewards are processed in ADHD. Method: Inside the scanner, 16 healthy children and 19 children with ADHD completed a spatial attention paradigm where cues informed about the availability of reward and feedbacks were provided about the earned reward. Results: In ventral anterior thalamus (VA), the controls exhibited greater activation in response to reward-predicting cues, as compared with no-reward cues, whereby in the ADHD group, the reverse pattern was observed (nonreward > reward). For feedbacks; absence of rewards produced greater activation than presence in the left caudate and frontal eye field for the control group, whereas for the ADHD group, the reverse pattern was again observed (reward > nonreward). Discussion: The present findings indicate that ADHD is associated with difficulty integrating reward contingency information with the orienting and regulatory phases of attention.

A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

Classification of psychiatric disorders is becoming one of the major focuses of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory outcomes using biological biomarkers. The use of quantitative electroencephalography (EEG) cordance has enhanced the clinical utility of the EEG in psychiatric and neurological subjects. Trichotillomania (TTM), a kind of body focused repetitive behavior, is defined as a disorder characterized by repetitive hair pulling that results in noticeable hair loss. Phenomenological observations underline similarities between hair-pulling behaviors and compulsions seen in obsessive-compulsive disorder (OCD). Despite the recognized similarities between OCD and TTM, there is evidence of important differences between these two disorders. In order to dichotomize the subjects of each disorder, artificial intelligence approach was employed using quantitative EEG (QEEG) cordance values with 19 electrodes from 10 brain regions (prefrontal, frontocentral, central, left temporal, right temporal, left parietal, occipital, midline, left frontal and right frontal) in 4 frequency bands (delta, theta, alpha and beta). Machine learning methods, artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (k-NN) and Naive Bayes (NB), were used in order to classify 39 TTM and 40 OCD patients. SVM, with its relatively better performance, was then combined with an improved ant colony optimization (IACO) approach in order to select more informative features with less iterations. The noteworthy performance of the hybrid approach underline that it is possible to discriminate OCD and TTM subjects with 81.04% overall accuracy. We used cordance as a biomarker combining absolute and relative power of EEG spectra.We used ACO for feature selection of 19 electrodes from 10 brain regions in 4 frequency bands.We used an improved ACO (IACO) to reduce computational complexity.We used SVM to classify trichotillomania and OCD subjects using their cordance values.We increased overall classification accuracy from 67.12% to 81.04% and increased AUC value from 0.698 to 0.816 decreasing used features from 40 to 13.

Classification of major depressive disorder subjects using Pre-rTMS electroencephalography data with support vector machine approach

The combination of repetitive transcranial magnetic stimulation (rTMS) and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. Using pre-treatment cordance, a relatively new quantitative EEG method combining complementary information from absolute and relative power of EEG spectra, 55 major depression disorder (MDD) subjects were classified into responder or non-responder classes. In order to predict the response of rTMS treatment, support vector machine (SVM) based classification was carried out on pre-treatment cordance and the classification performance was evaluated using 6, 8 and 10-fold cross-validation (CV). Promising findings indicate that it is possible to classify rTMS treatment responders with 85.45% overall accuracy with a sensitivity of 82.35% and 0.925 area under receiver operating characteristics (ROC) curve value.