Bilateral repetitive transcranial magnetic stimulation ameliorated sleep disorder and hypothalamic–pituitary–adrenal axis dysfunction in subjects with major depression

Chen, Xing; Jiang, Fei; Yang, Qun; Zhang, Peiyun; Zhu, Haijiao; Liu, Chao; Zhang, Tongtong; Li, Weijun; Xu, Jian; Shen, Hongmei

doi:10.3389/fpsyt.2022.951595

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…The bilateral DLPFC has long been considered the target of neuromodulation therapy for MDD. 1,17,19,39,40 However, emotional processing is associated with hemispheric variations. Positively valenced emotions are associated with the left hemisphere, while negatively valenced emotions are associated with the right side.…”

Section: Discussionmentioning

confidence: 99%

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Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS‐EEG and pilot study

Hao,

Dai,

Wei

et al. 2024

Photoderm Photoimm Photomed

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BackgroundMajor depressive disorder (MDD) was a prevalent mental condition that may be accompanied by decreased excitability of left frontal pole (FP) and abnormal brain connections. An 820 nm tPBM can induce an increase in stimulated cortical excitability. The purpose of our study was to establish how clinical symptoms and time‐varying brain network connectivity of MDD were affected by transcranial photobiomodulation (tPBM).MethodsA total of 11 patients with MDD received 820 nm tPBM targeting the left FP for 14 consecutive days. The severity of symptoms was evaluated by neuropsychological assessments at baseline, after treatment, 4‐week and 8‐week follow‐up; 8‐min transcranial magnetic stimulation combined electroencephalography (TMS‐EEG) was performed for five healthy controls and five patients with MDD before and after treatment, and time‐varying EEG network was analyzed using the adaptive‐directed transfer function.ResultsAll of scales scores in the 11 patients decreased significantly after 14‐day tPBM (p < .01) and remained at 8‐week follow‐up. The time‐varying brain network analysis suggested that the brain regions with enhanced connection information outflow in MDD became gradually more similar to healthy controls after treatment.ConclusionsThis study showed that tPBM of the left FP could improve symptoms of patients with MDD and normalize the abnormal network connections.

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Section: Discussionmentioning

confidence: 99%

“…Existing studies implied that tPBM for MDD should be performed at a higher energy output, and our findings are consistent with this perspective. The bilateral DLPFC has long been considered the target of neuromodulation therapy for MDD 1,17,19,39,40 . However, emotional processing is associated with hemispheric variations.…”

Section: Discussionmentioning

confidence: 99%

Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS‐EEG and pilot study

Hao,

Dai,

Wei

et al. 2024

Photoderm Photoimm Photomed

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“…The visualizations completed for the top four continuous covariates were concordant with current literature: there is strong epidemiological evidence that sleep problems are heavily linked with depression. Multiple papers have found difficulty falling asleep and decreased hours of sleep with increased depression [41][42][43][44][45][46][47][48][49][50][51][52][53]. Additionally, depression has bene linked to lower quality sleep and increased day time exhaustion [31,34,46,54,55].…”

Section: Plos Onementioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

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Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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“… 45 Both anxiety disorders and depressive disorders are associated with abnormalities in the stress response system, where inhibition of the subgenual prefrontal cortex and activation of the amygdala stimulates the autonomic sympathetic axis and the HPA axis when the organism is exposed to a physical or psychological stressor. 46 Under repeated uncontrollable stress, the HPA axis negative feedback mechanism is abolished, leading to increased cortisol, increased amygdala reactivity, and reduced hippocampus activity. 47 While structural-functional changes in the hippocampus play a crucial role in the pathogenesis of anxiety and depression, and reduced functional connectivity of bidirectional connections between the subgenual prefrontal cortex and hypothalamus of the brain is observed in patients with depressive disorders.…”

Section: The Hpa Axis In Endocrine Factorsmentioning

confidence: 99%

New Insights into the Mutual Promotion of Rosacea, Anxiety, and Depression from Neuroendocrine Immune Aspects

Yang¹,

Cai²

2023

CCID

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Rosacea is a common chronic inflammatory skin disease with a complex etiology and undefined pathogenesis, and there is still a lack of targeted clinical treatment. Patients with rosacea are at a higher risk of anxiety and depression compared to the healthy population. Compared to skin conditions such as acne and psoriasis, rosacea has been much less studied in relation to multiple-etiology psychiatric disorders such as anxiety and depression. In contrast to the mainstream belief that the causal association between rosacea and psychiatric disorders is that rosacea increases the psychological burden of patients and thus triggers psychiatric disorders simply by altering their facial appearance, this review outlines the possible common mechanisms between rosacea and anxiety and depression disorders, starting from the pathophysiological mechanisms of transient receptor potential family cation channels, HPA axis, and Th1/Th17 cell polarization. It envisages the possibility of the neuroendocrine-immune interplay between rosacea and anxiety and depression, and new ideas on the complex causal relationship between rosacea and psychiatric disorders, offering more orientations to open up new therapeutic approaches for rosacea.

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Bilateral repetitive transcranial magnetic stimulation ameliorated sleep disorder and hypothalamic–pituitary–adrenal axis dysfunction in subjects with major depression

Cited by 6 publications

References 44 publications

Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS‐EEG and pilot study

Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS‐EEG and pilot study

Use of machine learning to identify risk factors for insomnia

New Insights into the Mutual Promotion of Rosacea, Anxiety, and Depression from Neuroendocrine Immune Aspects

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