The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression

Depression Clinical Trial

Official title:

The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05872607
• Other study ID #: zlei19810830
• Status: Recruiting
• Start date: January 1, 2022
• Est. completion date: December 31, 2024

Study information

• Verified date: February 2023
• Source: The First Hospital of Jilin University
• Contact: Lei Zhang, doctor
• Phone: 15804300150
• Email: zlei99[@]jlu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Depression is the second leading cause of disease burden in our country. It has serious effects on the physical and mental health of human beings, and about 30% of patients with depression are unresponsive or respond poorly to antidepressant treatment. Clinical practice is in a tough position of wanting objective measures of assessing depression. The applicant and her team have devoted many years to the basic and clinical research on habenular nucleus (Hb) accumulating a significant amount of experience from animal experiments and patients' magnetic resonance (MR) studies. These studies have demonstrated that the habenular nucleus is the key target area that is responsible for the pathophysiological changes in depression as well as its treatment. Small volumes and unsatisfactory contrast have been knotty problems in the MR imaging of Hb. In addition, time-consuming manual segmentation and lack of quantitative standards in conventional studies has impeded the advancement of Hb research. Fortunately, the development of high-resolution multi-parametric quantitative MR imaging and the extensive use of artificial intelligence (AI) technology in medical imaging can just provide powerful support for the imaging, segmentation and quantification of Hb. This project proposes to use high resolution MR anatomy of Hb combined with multimodal fusion to 1) construct a model for automatic 3D segmentation of Hb MR images based on the densely connected multichannel dilated convolutional neural networks; 2) sift out the quantitative imaging signatures related to the antidepressants' efficacy using the radiomics methodology, and in combination with clinical information, construct an individualized prediction model for treatment efficacy. Overall, this study focuses on the translation of basic research to clinical application in the hope of providing quantifiable objective imaging markers in clinical practice, facilitating clinical decision-making and bringing about individualized precise diagnosis and treatment.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 300
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Premiere depressed patients

Exclusion Criteria:

• Drug therapy modality change?Incomplete MR image data

Related Conditions & MeSH terms

• Depression
• Depressive Disorder

Locations

Country	Name	City	State
China	The First Hospital of Jilin University	Changchun	Jilin

Sponsors (1)

Lead Sponsor	Collaborator
The First Hospital of Jilin University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Multimodal MR data of habenula	To extract high-dimensional features of multimodal image data including MP2RAGE, T1MAP, T2 SPACE, DIR, QSM, T2 *	Baseline (Before medication)