Beta oscillations in major depression – signalling a new cortical circuit for central executive function

Li, Yuezhi; Cheng, Kang; Wei, Zhaoguo; Qu, Xingda; Liu, Tiebang; Zhou, Yuwei; Hu, Yong

doi:10.1038/s41598-017-18306-w

Cited by 50 publications

(38 citation statements)

References 39 publications

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“…The top part of Figure 4 indicates that the amplitude of the alpha rhythm (especially at approximately 10 Hz) of normal control and medicated depressive patient samples is greater than that of unmedicated depressive patients. This result is congruent with the previous findings that demonstrated that depressed patients showed a task-dependent decrease in alpha, and beta oscillations in centro-parieto-occipital sites [30], [32], [33]. The bottom part of Figure 4 demonstrates that the primary power of four rhythms of normal controls is focused on the PFC region, while the primary power of four rhythms of the medicated group and the unmedicated group is focused on bilateral frontal or parietal regions.…”

Section: ) Analysis Of the Features Learned By The Last Fully Connecsupporting

confidence: 91%

“…This result might indicate prefrontal cortex abnormalities in clinically depressed subjects. Many previous studies [33]- [35] have shown that the PFC region is a significant nerve center of thinking and behavior regulation in the brain and is emerging as likely being directly involved in clinical depression.…”

Section: ) Analysis Of the Features Learned By The Last Fully Connecmentioning

confidence: 99%

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HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

et al. 2020

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Electroencephalogram (EEG) measurement, being an appropriate approach to understanding the underlying mechanisms of the major depressive disorder (MDD), is used to discriminate between depressive and normal control. With the advancement of deep learning methods, many studies have designed deep learning models to improve the classification accuracy of depression discrimination. However, few of them have focused on designing a convolutional filter to learn features according to EEG activity characteristics. In this study, a novel convolutional neural network named HybridEEGNet that is composed of two parallel lines is proposed to learn the synchronous and regional EEG features, and further differentiate normal controls from medicated and unmedicated MDD patients. A tenfold cross validation method is used to train and test the model. The results show that HybridEEGNet achieves a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three-category classification. The result of EEG feature analysis indicates that the differences of spatial distributions and amplitude ranges in the alpha rhythm (especially at approximately 10 Hz) among three categories might be distinctive attributes for depression discrimination.

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Section: ) Analysis Of the Features Learned By The Last Fully Connecsupporting

confidence: 91%

Section: ) Analysis Of the Features Learned By The Last Fully Connecmentioning

confidence: 99%

HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

et al. 2020

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“…Additionally, there have been reports of increased parietotemporal alpha coherence [83, 84] and cortical synchrony within the brains of MDD subjects [85]. A reduction in alpha synchronization in the frontoparietal network of individuals with MDD has also been shown, which may directly contribute to impaired short-term memory retention [84].…”

Section: Aberrant Network Function In Major Depressive Disordermentioning

confidence: 99%

Hormonal regulation of circuit function: sex, systems and depression

Thériault

Perreault

2019

Biol Sex Differ

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Major depressive disorder (MDD) is a debilitating chronic illness that is two times more prevalent in women than in men. The mechanisms associated with the increased female susceptibility to depression remain poorly characterized. Aberrant neuronal oscillatory activity within the putative depression network is an emerging mechanism underlying MDD. However, innate sex differences in network activity and its contribution to depression vulnerability have not been well described. In this review, current evidence of sex differences in neuronal oscillatory activity, including the influence of sex hormones and female cycling, will first be described followed by evidence of disrupted neuronal circuit function in MDD and the effects of antidepressant treatment. Lastly, current knowledge of sex differences in MDD-associated aberrant circuit function and oscillatory activity will be highlighted, with an emphasis on the role of sex steroids and female cycling. Collectively, it is clear that there are significant gaps in the literature regarding innate and pathologically associated sex differences in network activity and that the elucidation of these differences is invaluable to our understanding of sex-specific vulnerabilities and therapies for MDD.

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“…In previous report, impaired memory and attention efficiency abnormally enhanced beta activity containing more short-range frontal connections as well as inter-hemispheric temporo-parietal connections in people with depression. This adaptive compensatory mechanism is also reflected in the deterioration of flexibility in cognitive control ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression

et al. 2020

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Background Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease. Method A “go/no-go” response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus ( r IFG), pre-supplementary motor area (preSMA), and left primary motor cortex ( l M1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features. Result PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13–30 Hz) and low gamma (30–50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between l M1 and preSMA in the beta band ( t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and r IFG in the low gamma band ( t = −2.612, p = 0.012, FDR corrected). The FC between l M1 and preSMA in the beta band was positively correlated with illness duration ( r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and r IFG in the low gamma band was negatively correlated with illness duration ( r = −0.509, p = 0.002, FDR corrected) and retardation factor scores ( r = −0.288, p = 0.022, uncorrected). Conclusion In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.

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Beta oscillations in major depression – signalling a new cortical circuit for central executive function

Cited by 50 publications

References 39 publications

HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

Hormonal regulation of circuit function: sex, systems and depression

Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression

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