Depressive Disorder, Major Clinical Trial
— MDDOfficial title:
Probing Prefrontal Cortex Hemodynamic Alterations During Facial Emotion Recognition for Major Depression Disorder Through Functional Near-infrared Spectroscopy
Verified date | February 2018 |
Source | Xijing Hospital |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
Status | Active, not recruiting |
Enrollment | 200 |
Est. completion date | December 31, 2018 |
Est. primary completion date | August 31, 2018 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years to 35 Years |
Eligibility |
Inclusion Criteria: - MDD group: All subjects were free of psychotropic medication for a minimum of 4 weeks; Psychiatric diagnosis was determined by DSM-IV criteria; Depressed subjects were included with 17-item Hamilton Rating Scale for Depression (HRSD) scores = 18; Mini-mental State Examination(MMSE) = 24; All subjects had normal or corrected-to-normal vision. - HC group: All subjects were free of psychotropic medication for a minimum of 4 weeks; All subjects did not have any mental, neurological or physical diseases;HRSD scores = 7; MMSE = 28; All subjects had normal or corrected-to-normal vision. Exclusion Criteria: - Exclusion criteria were any history of neurologic trauma resulting in loss of consciousness, any current neurologic disorder, any lifetime psychiatric disorder other than major depression in the MDD subjects, or any lifetime psychiatric disorder in control subjects. Careful attention was paid to excluding substance abuse disorders. |
Country | Name | City | State |
---|---|---|---|
China | Department of psychiatry, Xijing Hospital; Research Institute of Biomedical Engineering, Xi'an Jiaotong University | Xi'an | Shaanxi |
Lead Sponsor | Collaborator |
---|---|
Xijing Hospital | Health Science Center of Xi’an Jiaotong University |
China,
Takizawa R, Fukuda M, Kawasaki S, Kasai K, Mimura M, Pu S, Noda T, Niwa S, Okazaki Y; Joint Project for Psychiatric Application of Near-Infrared Spectroscopy (JPSY-NIRS) Group. Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage. 2014 Jan 15;85 Pt 1:498-507. doi: 10.1016/j.neuroimage.2013.05.126. Epub 2013 Jun 10. Erratum in: Neuroimage. 2015 Apr 1;109:530. — View Citation
Tsuchida A, Fellows LK. Are you upset? Distinct roles for orbitofrontal and lateral prefrontal cortex in detecting and distinguishing facial expressions of emotion. Cereb Cortex. 2012 Dec;22(12):2904-12. doi: 10.1093/cercor/bhr370. Epub 2012 Jan 5. — View Citation
Zwick JC, Wolkenstein L. Facial emotion recognition, theory of mind and the role of facial mimicry in depression. J Affect Disord. 2017 Mar 1;210:90-99. doi: 10.1016/j.jad.2016.12.022. Epub 2016 Dec 19. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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. | Change from baseline HAMD-17 scores at 6 months | |
Secondary | 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. | Difference at facial emotion recognition between day 1 and month 6. | |
Secondary | 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. | Difference at PSQI between day 1, month 3 and month 6. |
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