1
|
Liu J, Yang W, Ma Y, Dong Q, Li Y, Hu B. Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification. Comput Biol Med 2024; 171:108069. [PMID: 38394798 DOI: 10.1016/j.compbiomed.2024.108069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/25/2024]
Abstract
Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.
Collapse
Affiliation(s)
- Jingyu Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, and the School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Wenxin Yang
- School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, China
| | - Yulan Ma
- School of Automation Science and Electrical Engineering, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China
| | - Qunxi Dong
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, and the School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yang Li
- School of Automation Science and Electrical Engineering, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China.
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, and the School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| |
Collapse
|
2
|
Liu X, Zhang H, Cui Y, Zhao T, Wang B, Xie X, Liang S, Sha S, Yan Y, Zhao X, Zhang L. EEG-based major depressive disorder recognition by neural oscillation and asymmetry. Front Neurosci 2024; 18:1362111. [PMID: 38419668 PMCID: PMC10899403 DOI: 10.3389/fnins.2024.1362111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024] Open
Abstract
Background Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions. Methods In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model. Results Regression analysis found significant predictors of MDD to be beta2 (16-24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24-40 Hz) power in the Medial Occipital Cortex (MOC) (B = -12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12-16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8-12 Hz, B = 9.004, p = 0.013) and theta (4-8 Hz, B = -13.582, p = 0.008) with eyes closed were also significant predictors. Conclusion The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder. Limitations The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.
Collapse
Affiliation(s)
- Xinyu Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Haoran Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi Cui
- Gnosis Healthineer Co. Ltd., Beijing, China
| | - Tong Zhao
- Gnosis Healthineer Co. Ltd., Beijing, China
| | - Bin Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiaomeng Xie
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sixiang Liang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | | | - Xixi Zhao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| |
Collapse
|