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

Taş, Cumhur; Çebi, Merve; Tan, Oğuz; Hızlı-Sayar, Gokben; Tarhan, Nevzat; Brown, E. B.

doi:10.1016/j.jad.2014.10.001

Cited by 48 publications

(42 citation statements)

References 51 publications

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“…In the left hemisphere of the brain, the MDD group had higher coherence, especially in the parietal and temporal regions. Several studies have reported that there was abnormal EEG absolute power, hemispheric asymmetry, or coherence with depression patients in some specific frequencies [51]. Hinrikus [52] found that the controls with MDD had the increased coherence between some brain regions.…”

Section: Discussionmentioning

confidence: 99%

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Jing

et al. 2017

Complexity

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A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain's "resting-state." But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

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Section: Discussionmentioning

confidence: 99%

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Jing

et al. 2017

Complexity

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“…The loss of temporal synchronization in the frontal interhemispheric and right-sided frontolimbic neuronal networks suggested to be the unique features that distinguish between BD and UD in previous research [79]. In this context, the paper puts forward a study using two-step hybridized methodology: PSO algorithm for feature selection process and ANN for training process.…”

Section: Discussionmentioning

confidence: 97%

“…A recent study reported that deficient left hemisphere alpha power in BD and decreased inter- hemispheric theta coherence in UD could discriminate these two groups. That study also underlined that BD patients, as compared to UD, exhibited greater centraltemporal theta and parietal-temporal alpha and theta coherence [79]. So we used only theta and alpha activity data and removed delta and beta activity data in this study (green text will be removed).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence approach to classify unipolar and bipolar depressive disorders

Ergüzel

Sayar

Tarhan

2015

Neural Comput & Applic

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Machine learning approaches for medical decision-making processes are valuable when both high classification accuracy and less feature requirements are satisfied. Artificial neural networks (ANNs) successfully meet the first goal with its adaptive engine, while natureinspired algorithms are focusing on the feature selection (FS) process in order to eliminate less informative and less discriminant features. Besides engineering applications of ANN and FS algorithms, medical informatics is another emerging field using similar methods for medical data processing. Classification of psychiatric disorders is one of the major focus of medical informatics using artificial intelligence approaches. Being one of the most debilitating psychiatric diseases, bipolar disorder (BD) is frequently misdiagnosed as unipolar disorder (UD), leading to suboptimal treatment and poor outcomes. Thus, discriminating UD and BD at earlier stages of illness could therefore help to facilitate efficient and specific treatment. The use of quantitative electroencephalography (EEG) cordance as a biomarker has greatly enhanced the clinical utility of EEG in psychiatric and neurological subjects. In this context, the paper puts forward a study using two-step hybridized methodology: particle swarm optimization (PSO) algorithm for FS process and ANN for training process. The noteworthy performance of ANN-PSO approach stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected features from alpha and theta frequency bands with 89.89 % overall classification accuracy.

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“…There are various studies, which focus on frontal alpha activity and its relation to emotion and decrease of alpha activity induced by negative emotions (20). In some other studies, scholars focus on other disorders such as obsessive compulsive disorder (21) and mood disorders (22). Most of studies in this area focus on asymmetry, in this sense left frontal activity is consistent with negative emotion or mood disorders.…”

Section: Discussionmentioning

confidence: 99%

Variability of Frontal Alpha Wave in Response to Mood Induction via Visual Stimulus: A Quantitative Electroencephalographic Study

Rajabi¹,

Monajemi²,

Setareh³

et al. 2016

Iran J Psychiatry Behav Sci

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Background: One of the prime areas in psychiatry is concerned with assessing emotions. Assessment of physiologic responses can be attained by various approaches. One of them is analyzing Electroencephalography (EEG). Objectives: In current study, we aimed to assess Alpha wave in frontal region after inducing specific emotion by showing evocative video clips. Materials and Methods: After a baseline in Eye Open situation recording, we showed 5 video clips that each induced specific emotions from five major ones (Neutral, Happiness, Sadness, Anger, Fear) to 66 healthy individuals, including 33 males and 33 females between 20 -40 years old and EEG recording was continued simultaneously. Then, we analyzed their brain waves. Absolute power of Alpha wave Bands (Alpha 1 and Alpha 2) in frontal region (FZ, F4, F3) was analyzed via Wilcoxon test. Results: With respect to eye open situation, all video clips made significant change in Alpha 2 in F3 and F4 (P < 0.001) but not in Fz except for sadness. Sad inductive clip made a significant increase in Alpha 2 in Fz, also Alpha 1 in F3 and F4 (P < 0.001). Fz had no significant variability in all other emotions. Conclusions:The most trenchant impact with respect to Alpha band was sadness, and the most exclusive finding in FZ was Alpha 2. Nonetheless, in two other zones (F3, F4), Alpha 1 was exclusive and Alpha 2 variability was non-exclusive. It appears plausible that sadness activates neuron groups, which are involved in generating Alpha waves.

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

Cited by 48 publications

References 51 publications

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Artificial intelligence approach to classify unipolar and bipolar depressive disorders

Variability of Frontal Alpha Wave in Response to Mood Induction via Visual Stimulus: A Quantitative Electroencephalographic Study

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