Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition

Zhang, Bingtao; Wei, Dan; Yan, Guanghui; Lei, Tao; Cai, Haishu; Yang, Zhifei

doi:10.1016/j.cmpb.2022.107113

Cited by 14 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dense populations in the region and overwork caused by stressful lives are important reasons for the concentration of people with depression (WHO, 2017). Patients with depression suffer from depressed mood or impaired thinking and cognitive abilities (Zhang et al, 2022). Pharmacotherapy is the primary treatment of depression.…”

Section: Introductionmentioning

confidence: 99%

Effect of Cryptotanshinone on BDNF/TrkB Pathway in Hippocampus of Chronic Stress-induced Depression Rats

Lin,

Wu,

Liao

et al. 2024

Pharmacognosy Magazine

View full text Add to dashboard Cite

Background: Cryptotanshinone (CTS) is a monomer possessing a diverse array of pharmacological properties, primarily derived from Salvia miltiorrhiza. CTS was examined for its impact on rats with depression subjected to chronic unpredictable mild stress (CUMS) in this study. Materials and methods: Depression-like behavior of rats was established by isolated feeding combined with CUMS for 28 days, with CTS (7/14/21 mg/kg) or fluoxetine (5 mg/kg) administered once daily during this time. A comprehensive evaluation was performed, including body weight assessment, sucrose preference test, forced swimming test, and open field test. Hematoxylin-eosin staining was conducted to detect pathological changes in hippocampal CA1 neurons. Western blot analysis was employed to assess the brain-derived neurotrophic factor (BDNF), tyrosine kinase receptor B (TrkB), Ras-related C3 botulinum toxin substrate 1 (Rac1), and Cofilin expression levels in the hippocampus. Results: The depression rat model was successfully constructed. CTS can significantly improve impaired neurons in the hippocampal CA1 area, depression-like behaviors, and weight loss in CUMS-induced rats. Moreover, CTS reversed the down-regulation of BDNF/TrkB and Rac1/Cofilin in the CUMS-induced rats’ hippocampus. Conclusion: CTS demonstrates the therapeutic potential for depression by improving depression-like behaviors and ameliorating impaired neurons in the hippocampal CA1 area, and the mechanisms may involve the up-regulation of BDNF/TrkB and related signaling proteins Rac1/Cofilin.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of Cryptotanshinone on BDNF/TrkB Pathway in Hippocampus of Chronic Stress-induced Depression Rats

Lin,

Wu,

Liao

et al. 2024

Pharmacognosy Magazine

View full text Add to dashboard Cite

show abstract

Dual‐task enhanced global–local temporal–spatial network for depression recognition from facial videos

Shen,

Wu,

Xing

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryIn previous studies on facial video depression recognition, although convolutional neural network (CNN) has become a mainstream method, its performance still has room for improvement due to the insufficient extraction of global and local information and the neglect of the correlation of temporal and spatial information. This paper proposes a novel dual‐task enhanced global–local temporal–spatial network (DTE‐GLTS) to enhance the extraction capability of global and local features and deepen the analysis of temporal–spatial information correlation. We design a dual‐task learning mode that utilizes the data‐efficient image transformer (Deit) as the main body to learn the global features of video sequences and guides Deit to learn local features with the pre‐trained temporal–spatial fusion network (TSF). In addition, we propose the TSF mechanism to more effectively fuse temporal–spatial information in video sequences, strengthen the correlation between frames and pixels, and embed it in Resnet to form the TSF network. To the best of our knowledge, this is the first application of Deit and dual‐task learning mode in the field of facial video depression recognition. The experimental results on AVEC 2013 and AVEC 2014 show that our method achieves competitive performance, with mean absolute error/root mean square error (MAE/RMSE) scores of 6.06/7.73 and 5.91/7.68, respectively, while significantly reducing the number of parameters.

show abstract

Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

Yang

Wei

Liu

et al. 2023

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition

Cited by 14 publications

References 50 publications

Effect of Cryptotanshinone on BDNF/TrkB Pathway in Hippocampus of Chronic Stress-induced Depression Rats

Effect of Cryptotanshinone on BDNF/TrkB Pathway in Hippocampus of Chronic Stress-induced Depression Rats

Dual‐task enhanced global–local temporal–spatial network for depression recognition from facial videos

Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

Contact Info

Product

Resources

About