Neuroimaging signatures and a deep learning modeling for early diagnosing and predicting non-pharmacological therapy success for subclinical depression comorbid sleep disorders in college students
Two-scale neuroimaging signatures related to the sub-DMN underlying the antidepressant mechanisms of non-pharmacological treatments for subclinical depression. The HFBN model exhibited supreme capability in early diagnosing and predicting non-pharmacological treatment outcomes for subclinical depression, thereby promoting objective clinical psychological treatment decision-making.
Authors
- Yunan Guo (ORCID: https://orcid.org/0009-0006-8637-0740)
- Danian Li
- Xinyu Liang (ORCID: https://orcid.org/0000-0001-6132-3354)
- Jianjia Zhang (ORCID: https://orcid.org/0000-0001-5048-5606)
- Hanyue Zhang (ORCID: https://orcid.org/0000-0003-1947-3310)
- Xiaotong Wang (ORCID: https://orcid.org/0000-0002-5329-2663)
- Yujie Liu (ORCID: https://orcid.org/0000-0002-6093-9509)
- Luping Zhou (ORCID: https://orcid.org/0000-0001-8762-2424)
- Shijun Qiu (ORCID: https://orcid.org/0000-0002-1840-0462)
Institutions
- Guangzhou University of Chinese Medicine (CN)
- First Affiliated Hospital of Guangzhou University of Chinese Medicine (CN)
Publication Details
- Journal
- International Journal of Clinical and Health Psychology
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1016/j.ijchp.2024.100526
- Citations
- 2
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.02
Funders
- Strong
- National Natural Science Foundation of China
- China Scholarship Council
- Zhejiang University
- Guangzhou University of Chinese Medicine
- Guangzhou University
- Basic and Applied Basic Research Foundation of Guangdong Province