scholarly journals Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram:A pilot study

2019 ◽  
Author(s):  
Li Wu ◽  
Xue-Qin Wang ◽  
Teng-Fei Dong ◽  
Ling Lei ◽  
Su-Xia Li ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Right Hemisphere ◽  
Temporal Dynamics ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Major Depressive ◽  
Pharmacological Mechanism ◽  
Spatio Temporal ◽  
Eeg Features

Abstract Background: Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment.Methods: The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed whole tests were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment. Results: For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of band, accompanied by a significant decrease in the RE of band. Lempel-Ziv complexity and C0 complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as the whole sleep process. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Further analysis of brain region-specific targets found that the frontal cortex showed a more intense EEG response with escitalopram treatment than central and occipital cortices.Conclusions: These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

scholarly journals Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: A pilot study

2020 ◽  
Author(s):  
Li Wu ◽  
Xue-Qin Wang ◽  
Yong Yang ◽  
Teng-Fei Dong ◽  
Ling Lei ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Sleep Quality ◽  
Temporal Dynamics ◽  
Sleep Stages ◽  
Major Depressive ◽  
Pharmacological Mechanism ◽  
Left And Right ◽  
Spatio Temporal ◽  
Eeg Features

Abstract Background: Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment.Methods: The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks’ treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment. Results: For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) ofA band (0.5 - 2Hz), accompanied by a significant decrease in the RE ofb band (20 - 30Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices.Conclusions: The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

scholarly journals Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram:A pilot study

2020 ◽  
Author(s):  
Li Wu ◽  
Xue-Qin Wang ◽  
Yong Yang ◽  
Teng-Fei Dong ◽  
Ling Lei ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Sleep Quality ◽  
Temporal Dynamics ◽  
Sleep Stages ◽  
Major Depressive ◽  
Pharmacological Mechanism ◽  
Left And Right ◽  
Spatio Temporal ◽  
Eeg Features

Abstract Background: Previous studies have shown escitalopram is related to sleep quality. However， effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. T his study may help to reveal pharmacological mechanism underlying escitalopram treatment . Methods: The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks’ treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment. Results: For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of band (0.5 - 2Hz), accompanied by a significant decrease in the RE of band (20 - 30Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices. Conclusions: The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

Morning and evening TSH response to TRH and sleep EEG disturbances in major depressive disorder

2001 ◽  
Vol 25 (3) ◽  
pp. 534-547 ◽  
Author(s):  
Luc Staner ◽  
Fabrice Duval ◽  
Francoise Calvi-Gries ◽  
Marie-Claude Mokrani ◽  
Paul Bailey ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Sleep Eeg ◽  
Major Depressive

scholarly journals Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder

2022 ◽  
Vol 12 (1) ◽  
pp. 112
Author(s):  
Benjamin C. Gibson ◽  
Andrei Vakhtin ◽  
Vincent P. Clark ◽  
Christopher C. Abbott ◽  
Davin K. Quinn
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Left Hemisphere ◽  
Emotional Regulation ◽  
Emotional Processing ◽  
Right Hemisphere ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Hemispheric Differences ◽  
Major Depressive ◽  
The Right

Hemispheric differences in emotional processing have been observed for over half a century, leading to multiple theories classifying differing roles for the right and left hemisphere in emotional processing. Conventional acceptance of these theories has had lasting clinical implications for the treatment of mood disorders. The theory that the left hemisphere is broadly associated with positively valenced emotions, while the right hemisphere is broadly associated with negatively valenced emotions, drove the initial application of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD). Subsequent rTMS research has led to improved response rates while adhering to the same initial paradigm of administering excitatory rTMS to the left prefrontal cortex (PFC) and inhibitory rTMS to the right PFC. However, accumulating evidence points to greater similarities in emotional regulation between the hemispheres than previously theorized, with potential implications for how rTMS for MDD may be delivered and optimized in the near future. This review will catalog the range of measurement modalities that have been used to explore and describe hemispheric differences, and highlight evidence that updates and advances knowledge of TMS targeting and parameter selection. Future directions for research are proposed that may advance precision medicine and improve efficacy of TMS for MDD.

scholarly journals Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

2016 ◽  
Author(s):  
David M Schnyer ◽  
Peter C. Clasen ◽  
Christopher Gonzalez ◽  
Christopher G Beevers
Keyword(s):  
Major Depressive Disorder ◽  
White Matter ◽  
Depressive Disorder ◽  
Right Hemisphere ◽  
Diffusion Tensor ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
Predictive Information ◽  
Brain White Matter

AbstractUsing MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (70%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

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