Merve Cebi

EEG power, cordance and coherence differences between unipolar and bipolar depression

INTRODUCTIONnUnderstanding the biological underpinnings of unipolar (UD) and bipolar depression (BD) is vital for avoiding inappropriate treatment through the misdiagnosis of bipolar patients in their first depressive episode. One plausible way to distinguish between UD and BD is to compare EEG brain dynamics to identify potential neurophysiological biomarkers. Here we aimed to test group differences in EEG power, cordance and coherence values between UD and BD.nnnMETHODSnTwenty-five bipolar and 56 unipolar depression patients were recruited. Sociodemographic and clinical variables were collected in addition to resting state EEG. Data was analyzed with multivariate and repeated analyses of variance where parametric assumptions were met.nnnRESULTSnAccordingly, we did not find any differences in the EEG absolute power and frontal asymmetry indexes between UD and BD. Regarding cordance, significant group differences were observed in the right theta cordance values (p=0.031). Regarding coherence, BD patients (as compared to UD) exhibited greater central-temporal theta (p=0.003), and parietal-temporal alpha (p=0.007) and theta (p=0.001) coherence. Lastly, less alpha coherence in BD was present at right frontal-central (p=0.007) and central inter-hemispheric (p=0.019) regions.nnnCONCLUSIONSnOur results demonstrate that EEG cordance and coherence values have potential to discriminate between UD and BD. The loss of temporal synchronization in the frontal interhemispheric and right sided frontolimbic neuronal networks may be a unique feature that distinguishes between BD and UD.

A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders

Feature selection (FS) and classification are consecutive artificial intelligence (AI) methods used in data analysis, pattern classification, data mining and medical informatics. Beside promising studies in the application of AI methods to health informatics, working with more informative features is crucial in order to contribute to early diagnosis. Being one of the prevalent psychiatric disorders, depressive episodes of bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD), leading to suboptimal therapy and poor outcomes. Therefore discriminating MDD and BD at earlier stages of illness could help to facilitate efficient and specific treatment. In this study, a nature inspired and novel FS algorithm based on standard Ant Colony Optimization (ACO), called improved ACO (IACO), was used to reduce the number of features by removing irrelevant and redundant data. The selected features were then fed into support vector machine (SVM), a powerful mathematical tool for data classification, regression, function estimation and modeling processes, in order to classify MDD and BD subjects. Proposed method used coherence, a promising quantitative electroencephalography (EEG) biomarker, values calculated from alpha, theta and delta frequency bands. The noteworthy performance of novel IACO-SVM approach stated that it is possible to discriminate 46 BD and 55 MDD subjects using 22 of 48 features with 80.19% overall classification accuracy. The performance of IACO algorithm was also compared to the performance of standard ACO, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms in terms of their classification accuracy and number of selected features. In order to provide an almost unbiased estimate of classification error, the validation process was performed using nested cross-validation (CV) procedure.

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.

The impact of high-frequency repetitive transcranial magnetic stimulation on executive functioning of drug-free patients with treatment-resistant depression

ABSTRACT OBJECTIVES: The aim of the present study was to examine the impact of 25u2005Hz high-frequency repetitive transcranial magnetic stimulation (rTMS) on neuropsychological testing in treatment-resistant depression patients who were receiving no other concomitant medications for the treatment. METHODS: A total of 19 patients with treatment-resistant depression and 20 healthy controls were included in the study. A 25u2005Hz, 1000 pulse stimulation was set at 100% of the motor threshold and delivered 20 times for 2 s with 30 s intervals as 20 sessions to the depression group, and sham treatment was applied to the control group. Brief Psychiatric Rating Scale (BPRS), Stroop task, trail-making test (TMT), and Wisconsin card sorting test (WCST) were performed both before and 3 days after the rTMS treatment. Seventeen-item Hamilton Depression Rating Scale (HAMD) and Beck Depression Inventory (BDI) were obtained at baseline and after the rTMS treatment, as well. RESULTS: After the rTMS treatment, 52.6% (10 of 19 patients) met the response criteria (>50% improvement in HAMD score), with 5 (26.3%) patients meeting the criteria for remission of depression (HAMD scoreu2009≤u20098). None of the patients had a worsened HAMD score at the end of treatment. Reflecting the antidepressant effect of rTMS treatment, the mean BDI score, BPRS score, and Stroop task scores significantly differed following the treatment (pu2009<u2009.001, pu2009<u2009.001, and pu2009=u2009.017, respectively). TMT score difference did not reach statistical significance, whereas WCST scores showed significance in “correct responses” and “perseverative errors” categories (pu2009<u2009.05, and pu2009<u2009.05, respectively). None of the test scores at the end of rTMS treatment showed a significant difference when compared to baseline scores for the control group (pu2009>u2009.05, for all). CONCLUSIONS: Results suggest that rTMS can be used as a beneficial treatment option to ameliorate cognitive functions, especially executive functions. Patients had an improvement in depressive symptoms with the rTMS treatment without any concomitant medication, as well. Therefore, improvement in cognitive performance might be associated with improvement in depressive symptoms.

The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder

The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients’ clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.

Medication Effects on EEG Biomarkers in Attention-Deficit/Hyperactivity Disorder

EEG biomarkers have become increasingly used to aid in diagnosis of attention-deficit/hyperactivity disorder (ADHD). Despite several studies suggesting that EEG theta/beta ratio may help discriminating ADHD from other disorders, the effect of medications on theta/beta ratio is not known. Forty-three children with ADHD that were evaluated with quantitative EEG before and after methylphenidate were included in the study. Theta/beta ratio, theta and beta powers for whole brain, central, and frontal areas were calculated. Theta/beta power decreased significantly after treatment; however, this change was largely due to an increase in beta power, rather than a fall in theta power. The results suggest that beta power is sensitive to medication effects, while theta power remains as a trait biomarker unaffected by medication status. The value of EEG biomarkers for monitoring neuropsychological performance and clinical status should be explored by future studies.

Functional MRI as a preoperative predictor for memory performance following temporal lobectomy: A systematic review -

Most of the patients with temporal lobe epilepsy suffer from memory decline following anterior temporal lobectomy (ATL). Studies examining memory decline following ATL show that post operative memory decline can be predicted in advance through the pre operative determination of memory lateralisation. Therefore, preoperative memory lateralisation plays a crucial role for epileptic surgery. Recent research suggests that instead of WADA test which is known as an invasive and high-risk operation, functional MRI can be used as a non-invasive and repeatable method to lateralize memory in the brain and to predict post operative memory decline. The aim of this article is to review the utility of fmri in prediction of post operative memory decline and to summarize the results of recent memory lateralisation studies using fmri.