Major depression disorder (MDD) has high estimated lifetime prevalence rates of 16.6%. Currently, the diagnosis for the MDD mainly depends on patients' reports of symptoms, observed behaviors and disease course. Establishment of clinically useful biomarkers for the MDD diagnosis would enhance patient management and treatment effect, and lead to the therapies adjusted to the individual. However, no such biomarkers have been established up to now. Therefore, the development of objective and feasible biomarkers is of special significance and a great challenge for accurate and early diagnosis and treatment of depression, in order to overcome the limitations of relying on clinical interviews alone.The ability to correctly recognize emotional states from faces is instrumental for interpersonal engagement and social functioning. Impairments processing of facial emotional expressions and biased facial emotion detection are frequently found in the MDD patients. To date, the studies on neural mechanism of the facial emotion recognition of the MDD patients were mainly based on the functional magnetic resonance imaging (fMRI). Functional near-infrared spectroscopy (fNIRS) has not been applied for the facial emotion recognition for the depression patients up to now. To bridge the important gap in the literature, we used the fNIRS methodology to investigate the neural mechanisms of facial emotion recognition for the patients with depression. We hypothesize the physiological feature of the hemodynamic responses in prefrontal cortex measured by fNIRS under the task of face emotion recognition, including the difference of the median, the Mayer wave power, the mean cross wavelet coefficient, and the mean wavelet coherence coefficient, combined with the behavior measurement (behavior accuracy and response time), could provide a reliable and feasible diagnosis approach to differentiate patients with the MDD from healthy control (HC) subjects with acceptable sensitivity and specificity.
The fMRI technique for functional imaging were limited by the fact that the individuals need to be placed in an uncomfortable or unnatural setting and foreign, noisy, dark, or claustrophobic environment (e.g., lying in a supine position in a narrow gantry with the head restrained during the entire examination), for accurate measurement during the procedure, with relatively lower temporal resolution. In contrast, a multi-channel fNIRS machine provides a completely non-invasive, quiet and mobile measurement of brain function in ordinary clinical settings and allows patients to be comfortably seated in a normal posture in a well-lit room, with higher temporal resolution making it possible to obtain a recording of the actual time course of a hemodynamic epoch in response to a specific cognitive task (e.g., facial emotion recognition task in our study) in a single trial. Additionally, due to the small operating and maintenance costs associated with NIRS, it is possible to run fNIRS study with a large sample of participants.
Study Type
OBSERVATIONAL
Enrollment
200
Department of psychiatry, Xijing Hospital; Research Institute of Biomedical Engineering, Xi'an Jiaotong University
Xi'an, Shaanxi, China
Hamilton Depression Scale-17(HAMD-17) score alterations
The HAMD-17 scores are to evaluate the severity degree of depression, and the HAMD-17 scores reduction rate is used to evaluate the clinical treatment effect. If the reduction rate exceeds 50%, it indicates that the clinical treatment is effective.
Time frame: Change from baseline HAMD-17 scores at 6 months
facial emotion recognition
The ability to correctly recognize emotional states from faces is instrumental for interpersonal engagement and social functioning. Impairments processing of facial emotional expressions and biased facial emotion detection are frequently found in the MDD patients. Functional near-infrared spectroscopy (fNIRS) has not been applied for the facial emotion recognition for the depression patients up to now.
Time frame: Difference at facial emotion recognition between day 1 and month 6.
Pittsburgh Sleep Quality Index(PSQI)
PSQI was used to assess the quality of sleep for subjects in the recent month. The total score ranges from 0 to 21 points. The higher the score, the worse the sleep quality.
Time frame: Difference at PSQI between day 1, month 3 and month 6.
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.