Research progress on the characteristics of magnetoencephalography signals in depression.
10.7507/1001-5515.202406072
- Author:
Zhiyuan CHEN
1
;
Yongzhi HUANG
1
;
Haiqing YU
1
;
Chunyan CAO
2
;
Minpeng XU
1
;
Dong MING
1
Author Information
1. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.
2. Department of Neurosurgery, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China.
- Publication Type:English Abstract
- Keywords:
Connectivity;
Depression;
Magnetoencephalography;
Nonlinear dynamics;
Rhythm
- MeSH:
Humans;
Magnetoencephalography/methods*;
Brain/physiopathology*;
Depression/diagnosis*;
Electroencephalography;
Magnetic Resonance Imaging
- From:
Journal of Biomedical Engineering
2025;42(1):189-196
- CountryChina
- Language:Chinese
-
Abstract:
Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
